Attitude estimation apparatus and transportation machine

An attitude estimation apparatus for estimating the attitude of a movable body includes an attitude estimation unit for estimating the roll angle of the movable body and for using a calculation process to estimate the offset error for at least one of first and second angular velocity detection units and first, second and third acceleration detection units. The attitude estimation unit includes a plurality of Kalman filters that each receive at least two or more imaginary offset quantities for a detection unit of interest, the imaginary offset quantities being different from each other. Each of the Kalman filters uses detected values from the detection units, estimated values from the previous estimation operation and the imaginary offset quantities to calculate a likelihood, which indicates how reliable the estimated values are. The attitude estimation unit weights the estimated values from the Kalman filters based on the likelihood to estimate the roll angle of the movable body.

TECHNICAL FIELD

The present invention relates to an attitude estimation apparatus and a transportation machine including the same.

BACKGROUND ART

Various estimation apparatus for estimating the roll angle of a vehicle such as motorcycle have been proposed. For example, the direction of the lighting unit may be controlled based on a roll angle estimated by an estimation apparatus to direct light in an appropriate direction from the lighting unit regardless of the inclination of the vehicle.

Patent Document 1 describes a vehicle-attitude estimation apparatus that estimates the roll angle and pitch angle based on detected values of the front/rear acceleration, lateral acceleration, top/bottom acceleration, yaw acceleration, and roll angular velocity of the vehicle, an estimated value of the front/rear vehicle-body velocity, and an estimated value of the pitch angular velocity.

PRIOR ART DOCUMENTS

Patent Documents

Patent Document 1: JP 2009-73466 A

SUMMARY OF THE INVENTION

Problems to be Solved by the Invention

In recent years, it has been desired to improve the estimation accuracy of attitude estimation apparatus.

An object of the present invention is to realize an attitude estimation apparatus with improved estimation accuracy for the attitude of a movable body, and a transportation machine that includes the same.

Means for Solving the Problems

An attitude estimation apparatus according to one aspect of the present invention is an attitude estimation apparatus for estimating an attitude of a movable body. The attitude estimation apparatus includes a first angular velocity detection unit configured to detect a first angular velocity, the first angular velocity being an angular velocity of the movable body about a first axis; a second angular velocity detection unit configured to detect a second angular velocity, the second angular velocity being an angular velocity of the movable body about a second axis, the second axis being in a different direction than that of the first axis; a first acceleration detection unit configured to detect a first acceleration, the first acceleration being an acceleration of the movable body in a first direction; a second acceleration detection unit configured to detect a second acceleration, the second acceleration being an acceleration of the movable body in a second direction, the second direction being different from the first direction; a third acceleration detection unit configured to detect a third acceleration, the third acceleration being an acceleration of the movable body in a third direction, the third direction being different from the first and second directions; a velocity information detection unit configured to detect information about a moving velocity in a direction of advance of the movable body; and an attitude estimation unit configured to estimate a roll angle of the movable body and estimate a first offset error using a calculation process, the first offset error being an offset error in at least one of the first angular velocity detection unit, the second angular velocity detection unit, the first acceleration detection unit, the second acceleration detection unit, or the third acceleration detection unit.

In the present specification and claims, “ at least one of the first angular velocity detection unit, the second angular velocity detection unit, the first acceleration detection unit, the second acceleration detection unit, or the third acceleration detection unit” means any one first angular velocity detection unit, one second angular velocity detection unit, one first acceleration detection unit, one second acceleration detection unit, or one third acceleration detection unit, individually, or any combination of a first angular velocity detection unit, a second angular velocity detection unit, a first acceleration detection unit, a second acceleration detection unit and a third acceleration detection unit.

The attitude estimation unit includes a plurality of Kalman filters each configured to receive, as an imaginary offset quantity for the first offset error, an associated one of a plurality of values including at least two values different from each other.

The plurality of Kalman filters each use, in a current estimation operation, a detected value from the first angular velocity detection unit, a detected value from the second angular velocity detection unit, a detected value from the first acceleration detection unit, a detected value from the second acceleration detection unit, a detected value from the third acceleration detection unit, a detected value from the velocity information detection unit, an estimated value of the roll angle from a previous estimation operation, and the imaginary offset quantity to calculate the estimated value of the roll angle of the movable body and a likelihood in the current estimation operation, the likelihood representing a reliability of an estimation result.

The attitude estimation unit estimates the first offset error based on the likelihoods obtained from the plurality of Kalman filters and the imaginary offset quantities provided to the plurality of Kalman filters.

“Likelihood” as used herein means how reliable the quantity estimated by each Kalman filter is relative to the actual quantity. The higher the value of the likelihood for a Kalman filter, the higher the reliability of the quantity estimated by this Kalman filter; the lower the value of the likelihood for a Kalman filter, the lower the reliability of the quantity estimated by this Kalman filter. For example, the accuracy of the estimation may be improved by weighting the quantity estimated by each Kalman filter depending on the associated likelihood.

Each Kalman filter receives, in advance, a quantity relating to the offset error in a detection units of interest which is one of the detection units as “imaginary offset quantity”. A plurality of imaginary offset quantities provided to a plurality of Kalman filters are different from each other. Then, the estimated quantity and likelihood are calculated by the each Kalman filter when an “imaginary offset quantity” has been generated in the detection unit of interest. Here, the likelihood calculated by each Kalman filter indicates how close (or distant) the actual offset error for the detection unit of interest is relative to the imaginary offset quantity provided to the Kalman filter. That is, the likelihood indicates how close the imaginary offset quantity is to the actual offset error for the detection unit of interest. For example, the offset error for the detection unit of interest in each Kalman filter may be weighted depending on the associated likelihood and the resulting value may be used to calculate the estimated value of the actual offset error for the detection unit of interest.

This means that the attitude estimation unit is capable of estimating, in addition to the quantity estimated by each Kalman filter, a new quantity (in this case, “first offset error”). For example, the next estimation operation may be performed based on this estimated offset error to improve the estimation accuracy of the roll angle over conventional arrangements.

The attitude estimation unit may estimate the first offset error by weighting the imaginary offset quantity received by each of the plurality of Kalman filters based on the likelihood obtained from this one of the plurality of Kalman filters. For example, the imaginary offset quantity in each Kalman filter may be weighted depending on the likelihood of this Kalman filter and the resulting value may be used to determine the estimated value of the first offset error. The estimated value determined in this manner is the imaginary offset quantities from a plurality of Kalman filters integrated depending on the likelihood.

The attitude estimation unit may calculate the estimated value of the roll angle of the movable body based on the likelihoods obtained from the plurality of Kalman filters and the roll angles of the movable body obtained from the plurality of Kalman filters. For example, the roll angle of the movable body may be estimated by weighting the roll angles of the movable body obtained from the plurality of Kalman filters based on the likelihoods obtained from the plurality of Kalman filters. That is, the estimated value of the roll angle in each Kalman filter may be weighted depending on the likelihood of this Kalman filter and the resulting value may be used to determine the estimated value of the roll angle. The estimated value determined in this manner is estimated values of the roll angle from the plurality of Kalman filters integrated depending on the likelihood.

Each of the plurality of Kalman filters included in the attitude estimation unit may include: a first Kalman filter configured to receive, as the imaginary offset quantity, a value larger than a maximum possible value of an offset quantity for a detection unit of interest, the detection unit of interest being a detection unit with which the first offset error is associated; a second Kalman filter configured to receive, as the imaginary offset quantity, a value smaller than a minimum possible value of the offset quantity of the detection unit of interest; and a third Kalman filter configured to receive, as the imaginary offset quantity, a value between the minimum possible value and the maximum possible value of the offset quantity of the detection unit of interest.

The first offset error may be estimated while assuming a situation where the detection unit of interest has generated the largest offset error. This will improve the estimation accuracy for the offset error and likelihood for the detection unit corresponding to the detection unit of interest. This will improve the estimation accuracy for the roll angle of the movable body.

The attitude estimation unit may include four or more Kalman filters. If the imaginary offset quantities provided to the Kalman filters are different, the estimation accuracy for the offset error and likelihood for the detection unit corresponding to the detection unit of interest will be further improved.

The first offset error may be the offset error from one of the first acceleration detection unit, the second acceleration detection unit, and the third acceleration detection unit.

Each of the plurality of Kalman filters may calculate a second offset error in addition to the roll angle of the movable body and the likelihood, the second offset error being the offset error from one of the first angular velocity detection unit, the second angular velocity detection unit, the first acceleration detection unit, the second acceleration detection unit, and the third acceleration detection unit, other than the detection unit of interest. In this case, each of the Kalman filters may, in a current estimation operation, calculate the estimated value of the roll angle of the movable body, an estimated value of the second offset error and the likelihood using the detected value from the first angular velocity detection unit, the detected value from the second angular velocity detection unit, the detected value from the first acceleration detection unit, the detected value from the second acceleration detection unit, the detected value from the third acceleration detection unit, the detected value from the velocity information detection unit, the estimated value of the roll angle from the previous estimation operation, an estimated value of the second offset error from the previous estimation operation, and the imaginary offset quantity.

The attitude estimation unit may calculate the second offset error based on the likelihoods obtained from the plurality of Kalman filters and the second offset errors obtained from the plurality of Kalman filters.

The offset error from a detection unit other than the detection unit of interest (i.e. second offset error) may be estimated with improved accuracy. Since the attitude estimation unit can estimate the roll angle of the movable body taking account of the second offset error, estimation accuracy will be further improved.

The detection unit with which the second offset error is associated may be one or more detection units. That is, the second offset error may be the offset error from a detection unit other than the detection unit of interest. The detection unit other than the detection unit of interest is from at least one of the first angular velocity detection unit, the second angular velocity detection unit, the first acceleration detection unit, the second acceleration detection unit or the third acceleration detection unit.

The second offset error may include the offset errors from the first angular velocity detection unit and the second angular velocity detection unit.

The second offset error may include the offset error from a detection unit other than the detection unit of interest. The detection unit other than the detection unit of interest is from one of the first acceleration detection unit, the second acceleration detection unit and the third acceleration detection unit.

The first angular velocity detection unit may detect a roll angular velocity of the movable body, the second angular velocity detection unit may detect a yaw angular velocity of the movable body, and the second offset error may be the offset error from the first angular velocity detection unit, the offset error from the second angular velocity detection unit and the offset error from the first acceleration detection unit or the offset error from the second acceleration detection unit that is other than the first offset error.

In one embodiment, the first acceleration detection unit may detect an acceleration in a top-bottom direction of the movable body, the second acceleration detection unit may detect an acceleration in a left-right direction of the movable body, the third acceleration detection unit may detect an acceleration in a front-rear direction of the movable body, and the first offset error may be the offset error from the first acceleration detection unit or the offset error from the second acceleration detection unit.

The attitude estimation apparatus may include a third angular velocity detection unit configured to detect a third angular velocity, the third angular velocity being an angular velocity of the movable body about a third axis, the third axis being different from the first and second axes.

Each of the plurality of Kalman filters may, in a current estimation operation, calculate the estimated value of the roll angle of the movable body, an estimated value of the pitch angle of the movable body and the likelihood using the detected value from the first angular velocity detection unit, the detected value from the second angular velocity detection unit, a detected value from the third angular velocity detection unit, the detected value from the first acceleration detection unit, the detected value from the second acceleration detection unit, the detected value from the third acceleration detection unit, the detected value from the velocity information detection unit, the estimated value of the roll angle from the previous estimation operation, the estimated value of the pitch angle from the previous estimation operation, the imaginary offset quantity, and an estimated value of the offset error from the previous estimation operation. The attitude estimation unit may estimate the first offset quantity based on the likelihoods obtained from the plurality of Kalman filters and the imaginary offset quantities provided to the plurality of Kalman filters, estimate the roll angle of the movable body based on the likelihoods obtained from the plurality of Kalman filters and the roll angles of the movable body obtained from the plurality of Kalman filters, and estimate the pitch angle of the movable body based on the likelihoods obtained from the plurality of Kalman filters and the pitch angles of the movable body obtained from the plurality of Kalman filters.

The attitude estimation apparatus is capable of detecting values for six axes. Then, based on these detected values, in addition to the roll angle and pitch angle of the movable body, the offset error for at least one detection unit is estimated. Then, taking account of this estimated offset error, the roll angle and pitch angle of the movable body and the offset error are estimated. This will improve the estimation accuracy of the roll angle and pitch angle of the movable body.

Each of the plurality of Kalman filters may, in the current estimation operation, calculate the estimated value of the roll angle of the movable body, the estimated value of the pitch angle of the movable body and the likelihood using a characteristic equation in which a value of a predetermined function is a constant, the function having, as its elements, the detected value from the first angular velocity detection unit, the detected value from the second angular velocity detection unit, the detected value from the third angular velocity detection unit, the detected value from the first acceleration detection unit, the detected value from the second acceleration detection unit, the detected value from the third acceleration detection unit, the detected value from the velocity information detection unit, the estimated value of the roll angle from the previous estimation operation, the estimated value of the pitch angle from the previous estimation operation, the imaginary offset quantity, and the estimated value of the second offset error from the previous estimation operation.

A characteristic equation may be used to estimate an offset error for a new detection unit. This will improve the estimation accuracy of the roll angle and pitch angle of the movable body. The characteristic equation may be, for example, an equation derived from an equation of the rotary motion of the movable body about the first axis.

The attitude estimation apparatus may include a load estimation unit configured to estimate a load applied to at least one of a front wheel and a rear wheel included in the movable body, where the load estimation unit may estimate the load based on the detected value from the first angular velocity detection unit, the detected value from the second angular velocity detection unit, the detected value from the third angular velocity detection unit, the detected value from the first acceleration detection unit, the detected value from the second acceleration detection unit, the detected value from the third acceleration detection unit, the detected value from the velocity information detection unit, and the roll angle of the movable body, the pitch angle of the movable body and the first offset error estimated by the attitude estimation unit.

The attitude estimation apparatus may include a suspension stroke quantity estimation unit configured to estimate a stroke quantity of a suspension provided on at least one of the front wheel and the rear wheel of the movable body, where the load estimation unit may estimate the load applied to both the front wheel and the rear wheel provided on the movable body, and the suspension stroke quantity estimation unit may estimate the stroke quantity of the suspension based on an estimated value of the load applied to both the front wheel and the rear wheel estimated by the load estimation unit.

The attitude estimation apparatus may include an elevation/depression angle estimation unit configured to estimate an elevation/depression angle based on an estimated value of the stroke quantity of the suspension estimated by the suspension stroke quantity estimation unit, the elevation/depression angle being an angle between an axis in a vehicle-body coordinate system fixed to the movable body and an axis in a road-surface coordinate system fixed to a road surface in contact with the front wheel or the rear wheel.

Since the above-described embodiment may estimate the elevation/depression angle of the movable body, the attitude of the movable body will be estimated more accurately.

(Each of the plurality of Kalman filters may calculate the estimated value of the roll angle of the movable body, the estimated value of the pitch angle of the movable body, and the likelihood taking account of an estimated value of the elevation/depression angle estimated by the elevation/depression angle estimation unit.

An attitude estimation apparatus may further include a slope estimation unit configured to estimate a longitudinal slope of the road surface on which the movable body is placed based on an estimated value of the elevation/depression angle estimated by the elevation/depression angle estimation unit and the estimated values of the roll angle and the pitch angle of the movable body estimated by the attitude estimation unit.

The above-described arrangement will estimate the slope of the road surface more accurately.

A transportation machine including the movable body and the attitude estimation apparatus based on one of the embodiments described above is included in embodiments of the present invention.

Effects of the Invention

Embodiments of the present invention will improve the estimation accuracy for the attitude of a movable body.

EMBODIMENTS FOR CARRYING OUT THE INVENTION

Embodiments of the present invention will be described with reference to the drawings. In the drawings referred to below, the size ratios in the drawings and the actual size ratios are not necessarily identical.

The coordinate axes will be defined below with reference to the drawings.FIG. 1illustrates the relationship between the basis vector eoof an inertia coordinate system, the basis vector ebof a vehicle coordinate system, and the basis vector erof a road-surface coordinate system. In the following formulas, vectors may be expressed by bold letters. Each coordinate system includes an x-axis, a y-axis, and a z-axis. InFIG. 1, the vehicle100that represents a reference with respect to which a vehicle coordinate system and a road-surface coordinate system are set is a motorcycle.
eo=(eox,eoy,eoz)
eb=(ebx,eby,ebz)
er=(erx,ery,erz)   [Formula 1]

The inertia coordinate system eois a coordinate system fixed to the horizontal plane of the earth, and the z-axis is defined as the upward vertical direction.

In the vehicle coordinate system eb, when the vehicle is standing upright on the horizontal road surface, the x- and y-axes are in the horizontal plane and the x-axis is fixed to the vehicle body so as to be the forward direction with respect to the vehicle. The angle formed by the vehicle coordinate system eband the inertia coordinate system eochanges as the suspension of the vehicle moves. The suspension is a shock absorber provided between the vehicle wheels2and3and the vehicle body1.

In the road-surface coordinate system er, the y-axis is the same as the y-axis of the vehicle coordinate system eb, and the vehicle coordinate system ebhas been rotated about the y-axis such that the direction extending from the grounding point P3between the rear wheel3and road surface200to the grounding point P2between the front wheel2and road surface200is the same as the x-axis.

As used herein, “yaw angle” means a rotational angle about the z-axis of the inertia coordinate system eo(eoz), “yaw angular velocity” means a rate of change over time of the “yaw angle”, and “yaw angular acceleration” means a rate of change over time of the yaw angular velocity. As used herein, “pitch angle” means a rotational angle about the y-axis of the inertia coordinate system eo(eoy), “pitch angular velocity” means a rate of change over time of the “pitch angle”, and “pitch angular acceleration” means a rate of change over time of the pitch angular velocity. As used herein, “roll angle” means a rotational angle about the x-axis of the vehicle coordinate system eb(ebx), “roll angular velocity” means a rate of change over time of the “roll angle”, and “roll angular acceleration” means a rate of change over time of the roll angular velocity.

As used herein, “top-bottom direction” means the direction of the z-axis of the vehicle coordinate system eb(ebz), “front-rear direction” means the direction of the x-axis of the vehicle coordinate system eb(ebx), and “left-right direction” means the direction of the y-axis of the vehicle coordinate system eb(eby).

Roll angle, roll angular velocity, roll angular acceleration, yaw angle, yaw angular velocity, yaw angular acceleration, pitch angle, pitch angular velocity, pitch angular acceleration, top-bottom acceleration, front-rear acceleration, and left-right acceleration will be denoted by the following characters. One dot on the character denoting a parameter means first-order temporal differential.

FIG. 2is a schematic view of a vehicle including an attitude estimation apparatus according to the present embodiment. The vehicle100shown inFIG. 2is a motorcycle.

As shown inFIG. 2, the vehicle100includes a vehicle body1. A front wheel2is attached to a front portion of the vehicle body1, and a rear wheel3is attached to a rear portion of the vehicle body1. Further, a group of sensors5are attached to a central portion of the vehicle body1. The group of sensors5will be described in detail further below.

A rear-wheel velocity sensor7is attached to the wheel body of the rear wheel3for detecting the rotational velocity of the rear wheel3. According to the present embodiment, the rear-wheel velocity sensor7corresponds to the “velocity information detection unit”.

Handlebars11are attached to the top of the front portion of the vehicle body1so as to be swingable to the left and right. In the implementation shown inFIG. 1, a navigation system12is provided near the handlebars11, and a headlight14and a headlight driving device15are provided on the front portion of the vehicle body1. The headlight driving device15controls the direction of the headlight14. An electronic control unit20is provided on the rear portion of the vehicle body1. The electronic control unit20will be referred to simply as “ECU20” as appropriate below. The positions of the ECU20and the group of sensors5are not limited to those inFIG. 2.

Output signals from the group of sensors5and rear-wheel velocity sensor7are provided to the ECU20. The ECU20controls the various parts of the vehicle body1, and estimates the roll angle and other parameters of the vehicle body1and provide these estimated values to the navigation system12and headlight driving device15, for example.

According to the present embodiment, the group of sensors5, rear-wheel velocity sensor7and ECU20constitute the attitude estimation apparatus.

<Configuration of Attitude Estimation Apparatus>

FIG. 3is a schematic block diagram of an exemplary configuration of the attitude estimation apparatus. The attitude estimation apparatus10shown inFIG. 3includes the group of sensors5, the rear-wheel velocity sensor7, low-pass filters31,32,34,35and36, differentiators41and42, and a calculation unit49. The functionality of the calculation unit49is realized by the ECU20shown inFIG. 2and a program. The calculation unit49corresponds to the “attitude estimation unit”.

The group of sensors5include a roll angular velocity sensor21, a yaw angular velocity sensor22, a top-bottom acceleration sensor24, a front-rear acceleration sensor25and a left-right acceleration sensor26.

The roll angular acceleration sensor21is provided on the vehicle body1so as to detect the roll angular velocity of the vehicle body1. The yaw angular velocity sensor22is provided on the vehicle body1so as to detect the yaw angular velocity of the vehicle body1. According to the present embodiment, the roll angular velocity sensor21corresponds to the “first angular velocity detection unit” and the yaw angular velocity sensor22corresponds to the “second angular velocity detection unit”.

The top-bottom acceleration sensor24is provided on the vehicle body1so as to detect the acceleration in the top-bottom direction of the vehicle body1. The front-rear acceleration sensor25is provided on the vehicle body1so as to detect the acceleration in the front-rear direction of the vehicle body1. The left-right acceleration sensor26is provided on the vehicle body1so as to detect the acceleration in the left-right direction of the vehicle body1. According to the present embodiment, the top-bottom acceleration sensor24corresponds to the “first acceleration detection unit”, the front-rear acceleration sensor25corresponds to the “second acceleration detection unit”, and the left-right acceleration sensor26corresponds to the “third acceleration detection unit”.

In this exemplary implementation, the top-bottom acceleration sensor24, front-rear acceleration sensor25and left-right acceleration sensor26detect the accelerations in directions that are perpendicular to each other. This is not essential and it is only required that the accelerations in at least three different directions be detected.

The output signal from the roll angular velocity sensor21is passed through the low-pass filter31and is fed into the calculation unit49and differentiator41as a roll angular velocity. The low-pass filter31removes noise from the output signal from of the roll angular velocity sensor21. The differentiator41provides, to the calculation unit49, the differential value of the roll angular velocity as a roll angular acceleration.

The output signal from the yaw angular velocity sensor22is passed through the low-pass filter32and is fed into the calculation unit49and differentiator42as a yaw angular velocity. The low-pass filter32removes noise from the output signal from the yaw angular velocity sensor22. The differentiator42provides, to the calculation unit49, the differential value of the yaw angular velocity as a yaw angular acceleration.

The output signal from the top-bottom acceleration sensor24is passed through the low-pass filter34and is provided to the calculation unit49as a top-bottom acceleration. The output signal from the front-rear acceleration sensor25is passed through the low-pass filter35and is fed into the calculation unit49as a front-rear acceleration. The output signal from the left-right acceleration sensor26is passed through the low-pass filter36and is provided to the calculation unit49as a left-right acceleration.

The frequency characteristics of the low-pass filters31,32,34,35and36are decided depending on the output characteristics of the corresponding sensors21,22,24,25and26. More specifically, the frequency characteristics of the noise components contained in the output signals from the sensors21,22,24,25and26can be identified in advance, at the stage of apparatus design. The low-pass filters31,32,34,35and36may be designed to block these noise components and pass the detection signals of the sensors21,22,24,25and26, which will be needed.

The output signal from the rear-wheel velocity sensor7is fed into the calculation unit49as a rear-wheel velocity. The rear-wheel velocity is a rotational velocity of the outermost periphery of the tire, assuming that there is no slide between the road surface and the tire of the rear wheel3and, in reality, calculated based on the output signal of the rear-wheel velocity sensor7and the size of the tire. To simplify the explanation,FIG. 3shows a signal indicative of rear-wheel velocity as if it were provided by the rear-wheel velocity sensor7.

The calculation unit49receives detected values relating to roll angular velocity, roll angular acceleration, yaw acceleration, yaw angular acceleration, top-bottom acceleration, front-rear acceleration, left-right acceleration, and rear-wheel velocity. Based on these values, the calculation unit49estimates the roll angle, front-rear direction vehicle velocity, roll angular velocity sensor offset, yaw angular velocity sensor offset, left-right acceleration sensor offset and top-bottom acceleration sensor offset before outputting them.

The roll angular velocity sensor offset is the offset error for the roll angular velocity sensor21. The yaw angular velocity sensor offset is the offset error for the yaw angular velocity sensor22. The left-right acceleration sensor offset is the offset error for the left-right acceleration sensor26. The top-bottom acceleration sensor offset is the offset error for the top-bottom acceleration sensor24.

The rear-wheel velocity detected by the rear-wheel velocity sensor7, and the front-rear direction vehicle velocity, roll angular velocity sensor offset, yaw angular velocity sensor offset, left-right acceleration sensor offset, and top-bottom acceleration sensor offset estimated by the calculation unit49will be denoted by the following characters.

FIG. 4is a schematic block diagram of an exemplary configuration of the calculation unit49. As shown inFIG. 4, the calculation unit49includes a plurality of Kalman filters50_1,50_2and50_3, and a state quantity determination unit503. In the following description, the Kalman filters50_1,50_2and50_3may be collectively referred to as “Kalman filters50”.

FIG. 4shows an implementation where the calculation unit49includes three Kalman filters50; however, this number is merely an example. The calculation unit49may include two Kalman filters50, or four or more Kalman filters50.

The Kalman filters50(50_1,50_2and50_3) use a kinematic model for the vehicle100, described below.

InFIG. 4, the Kalman filters50(50_1,50_2and50_3) each include a system equation calculation unit51, an observation equation calculation unit52, a subtractor53, an adder54, an integrator55, a Kalman gain calculation unit56, a likelihood calculation unit57, a low-pass filter58, and an imaginary offset input unit59.

The system equation that is an equation used for the calculation at the system equation calculation unit51includes the function f(x,u). The observation equation that is an equation used for the calculation at the observation equation calculation unit52includes the function h(x,u). The Kalman gain calculation unit56includes a fifth-degree Kalman gain K.

The imaginary offset input unit59provides, as an offset value for a predetermined one of the sensors of the group5, a predetermined value for each of the Kalman filters50(50_1,50_2and50_3) to the observation equation calculation unit52. According to the present embodiment, the imaginary offset input unit59is configured to provide the offset value byfor the left-right acceleration sensor26to the observation equation calculation unit52. In this implementation, the left-right acceleration sensor26corresponds to the “detection unit of interest”, or in other words, in this example the left-right acceleration sensor26is a detection unit associated with the offset error whose imaginary offset quantity is provided in each of the Kalman filters50(50_1,50_2and50_3).

InFIG. 4, the offset value for the left-right acceleration sensor26provided by the imaginary offset input unit59of the Kalman filter50_1to the observation equation calculation unit52is denoted by by. Similarly, the offset value for the left-right acceleration sensor26provided by the imaginary offset input unit59of the Kalman filter50_2to the observation equation calculation unit52is denoted by by2. The offset value for the left-right acceleration sensor26provided by the imaginary offset input unit59of the Kalman filter50_3to the observation equation calculation unit52is denoted by by3.

The likelihood calculation unit57determines a likelihood using calculation, the likelihood being indicative of the reliability of the result from the estimation by the Kalman filter50(50_1,50_2or50_3). The low-pass filter58is a calculation unit for filtering the value calculated by the likelihood calculation unit57. The low-pass filter58may be replaced by any other calculation unit that can implement the same functionality. It is optional whether the low-pass filter58is included. Examples of the calculation performed by the likelihood calculation unit57will be described further below.

The calculation units51,52,53,54,55,56,57and58constituting a Kalman filter50may be implemented by, for example, a program that has been prepared in advance being performed by the ECU20. Alternatively, some or all of the calculation units51,52,53,54,55,56,57and58may be implemented by independent hardware mounted.

In the current estimation operation, the following values are provided as the input parameter u of the function f(x,u) included in the system equation is the detected value of the roll angular velocity ωro, the detected value of the roll angular acceleration (differential value of the roll angular velocity ωro), the detected value of the yaw angular velocity ωya, the yaw angular acceleration (differential value of the yaw angular velocity ωya), and the detected value of the front-rear acceleration Gx.

Further, the following values are provided as the input parameter x of the function f(x,u) included in the system equation: the estimated value of the roll angle φ, the estimated value of the vehicle velocity Vx, the estimated value of the roll angular velocity sensor offset bro, the estimated value of the yaw angular velocity sensor offset bya, and the estimated value of the top-bottom acceleration sensor offset bzfrom the previous estimation operation.

The output from the system equation calculation unit51is the predicted differential value of the roll angle φ, the predicted differential value of the vehicle velocity Vx, the predicted differential value of the roll angular velocity sensor offset bro, the predicted differential value of the yaw angular velocity sensor offset bya, and the predicted differential value of the top-bottom acceleration sensor offset bz.

The adder54adds the fifth-degree Kalman gain K obtained by the previous estimation operation to the predicted differential value of the roll angle φ, the predicted differential value of the vehicle velocity Vx, the predicted differential value of the roll angular velocity sensor offset bro, the predicted differential value of the yaw angular velocity sensor offset bya, and the predicted differential value of the top-bottom acceleration sensor offset bz.

The predicted differential value of the roll angle φ, the predicted differential value of the vehicle velocity Vx, the predicted differential value of the roll angular velocity sensor offset bro, the predicted differential value of the yaw angular velocity sensor offset bya, and the predicted differential value of the top-bottom acceleration sensor offset bzto which the Kalman gain K has been added are integrated by the integrator55. This provides the estimated value of the roll angle φ, the estimated value of the vehicle velocity Vx, the estimated value of the roll angular velocity sensor offset bro, the estimated value of the yaw angular velocity sensor offset bya, and the estimated value of the top-bottom acceleration sensor offset bzfrom the current estimation operation.

The following values are provided as the input parameter x of the function h(x,u) included in the observation equation: the estimated value of the roll angle φ, the estimated value of the vehicle velocity Vx, the estimated value of the roll angular velocity sensor offset bro, the estimated value of the yaw angular velocity sensor offset bya, and the estimated value of the top-bottom acceleration sensor offset bz. Further, as discussed above, the offset value for left-right acceleration sensor26provided by the imaginary offset input unit59is provided as the input parameter x of the function h(x,u) included in the observation equation.

The output from the observation equation calculation unit52is the calculated value of the top-bottom acceleration Gz, the calculated value of the left-right acceleration Gy, and the calculated value of the rear-wheel velocity vr.

The Kalman filters50each receive, as the input parameter y, the detected value of the top-bottom acceleration Gz, the detected value of the left-right acceleration Gy, and the detected value of the rear-wheel velocity vr. The Kalman gain calculation unit56calculates the Kalman gain K based on the difference between the detected values of the top-bottom acceleration Gz, left-right acceleration Gyand rear-wheel velocity vrand the calculated values (this difference may be hereinafter referred to as “observation/prediction error e”).

Finding this kinematic model's system equation f(x,u) and observation equation h(x,u) gives the relational expression for the input parameters u and y and the output parameter x.

<Finding of System Equation and Observation Equation>

The present embodiment supposes the following points to simplify the kinematic model.

(a) There is no rotational slide between the rear wheel3and road surface.

(b) The side-skid velocity of the rear wheel3is zero.

(c) The road surface is flat. “Flat” as used herein means a plane without irregularities, and may be inclined.

Based on suppositions (a) to (d), the kinematic model equation is found in the following manner. The differential value of the yaw angle, the differential value of the pitch angle, and the differential value of the roll angle will be denoted by the following characters.

First, the following equation may be established based on a common relational expression between Euler angle and angular velocity.

Supposition (d) provided above means that the pitch angle θ and its differential value are zero. Thus, the following equation is derived from equation (1).

Based on the second row of equation (2), the pitch angular velocity ωpimay be deleted. This gives the following equation.

FIG. 5illustrates where the group of sensors5are mounted. InFIG. 5, (a) shows the left side of the vehicle100while (b) shows the front of the vehicle100.FIG. 6is a schematic cross-sectional view of the rear wheel3.FIG. 7illustrates where the group of sensors5are mounted by means of vectors.

InFIG. 5, the position at which the group of sensors5are mounted is denoted by PS. The horizontal distance between the mounting position PS and the center of the rear wheel3is denoted by L, and the height of the mounting position PS relative to the road surface200is denoted by h.

The positional vector of the mounting position PS for the group of sensors5relative to the origin O of the inertia coordinate system, the positional vector of the grounding point P3of the rear wheel3relative to the origin O of the inertia coordinate system, the vector from the grounding point P3of the rear wheel3to the mounting position PS for the group of sensors5, and the second-order differential vector of various vectors will be denoted by the following characters. The two dots on top of the character indicating a parameter means second-order temporal differential.

TABLE 4ParameterMeaningrpositional vector of mounting position PSfor group of sensors 5{umlaut over (r)}second-order differential vector of positional vector rr0positional vector of grounding point P{umlaut over (r)}0second-order differential vector of positional vector r0ρvector from grounding point P to mounting position PSof group of sensors 5{umlaut over (ρ)}second-order differential vector of vector ρ

Gravity acceleration vector and acceleration vector detected at the mounting position PS will be denoted by the following characters. The acceleration vector G is detected by the top-bottom acceleration sensor24provided at the mounting position PS, front-rear acceleration sensor25and left-right acceleration sensor26.

TABLE 5ParameterMeaninggGravity acceleration vectorGacceleration vector at mounting position PS forgroup of sensors 5

The acceleration vector G is obtained by Equation (5) below.
[Formula 6]
G={umlaut over (r)}+g={umlaut over (r)}0+{umlaut over (ρ)}+g(5)

The right side of Equation (5) will be calculated below. The vector ρ shown in FIG7is expressed by the following equation.
[Formula 7]
ρ=[eb]ρ=[ebx,eby,ebz]ρ  (6)

As discussed above, the vector ebis the basis vector of the vehicle coordinate system, where ebxcorresponds to the component in the forward direction with respect to the vehicle body1, ebycorresponds to the component in the left direction with respect to the vehicle body1, and ebzcorresponds to the component in the upward direction with respect to the vehicle body1. Based onFIGS. 6 and 7, the matrix ρ shown in the right side of Equation (6) is expressed by the equation below. InFIG. 7, the radius of a cross section of the rear wheel3is denoted by Rcr, and the radius of the rear wheel3is denoted by Re. As discussed above, the roll angle of the vehicle body1is denoted by φ.

When movements of the suspension of the vehicle100are considered, to speak exactly, L and h vary; however, such variances are sufficiently small compared with the values of L and h, and thus the values of L and h can be considered approximately constant.

From Equation (7) provided above, the second-order differential vector of the vector ρ can be expressed by the following equation.

In Equation (8), ax, ayand azare functions. The functions ax, ayand azcan be determined by calculating Equations (6) and (7). Using Equation (1) provided above to rearrange Equation (7) into Equation (8) removes the differential value of the roll angle φ and the differential value of the yaw angle ψ.

Next, the side-skid velocity of the vehicle100is denoted by Vy. The first-order differential vector of the positional vector r0inFIG. 7is expressed by the following equation using the vehicle velocity Vxand side-skid velocity Vy. The vector e0is the basis vector of the inertia coordinate system.

From Supposition (b), Vy=0; when Equation (9) is first-order-differentiated, the second-order differential vector of the positional vector r is expressed by the following equation.

Next, the gravity acceleration vector may be expressed by the following equation. g in the right side of Equation (11) indicates the magnitude of the gravity acceleration.

Based on Equations (5), (8), (10) and (11), the acceleration vector G detected at the mounting position PS may be expressed by the following equation.

The acceleration vector G detected at the mounting position PS may be expressed by the following equation using the front-rear acceleration Gxdetected by the front-rear acceleration sensor25, the left-right acceleration Gydetected by the left-right acceleration sensor26, and the top-bottom acceleration Gzdetected by the top-bottom acceleration sensor24.

Thus, based on Equations (12) and (13), the front-rear acceleration Gx, the left-right acceleration Gyand the top-bottom acceleration Gzmay be expressed by the following equation.

Next, the relationship between the rear-wheel velocity vrand vehicle velocity Vxis determined. Supposition (a) means that there is no slide between the rear wheel3and road surface200, and thus the relationship expressed by the following equation can be established between the rear-wheel velocity vrand vehicle velocity Vx.

Equation (15) gives the following equation.

Equations (1), (14) and (16) give the following equations.

Each of the Kalman filters50, at the system equation calculation unit51, uses Equation (17) as the system equation to perform calculation, and, at the observation equation calculation unit52, uses Equation (18) as the observation equation to perform calculation.

Even if the roll angular velocity offset bro, yaw angular velocity offset bya, pitch angular velocity offset bpiand top-bottom acceleration offset bzvary, such variances are small compared with movements of the vehicle100. Thus, the differential value of the roll angular velocity offset bro, the differential value of the yaw angular velocity offset bya, the differential value of the pitch angular velocity offset bpi, and the differential value of the top-bottom acceleration offset bzcan be considered to be zero.

Further, replacing some values in Equations (17) and (18) with values reflecting offset errors gives the following equations.

When the offset errors are taken into consideration, each of the Kalman filters50, at the system equation calculation unit51, uses Equation (19) as the system equation to perform calculation, and, at the observation equation calculation unit52, uses Equation (20) as the observation equation to perform calculation. Equations (19) and (20) take account of the roll angular velocity offset bro, yaw angular velocity offset bya, and top-bottom acceleration offset bz.

As the Kalman filters50use Equation (19) as the system equation and use Equation (20) as the observation equation to perform calculation, the roll angle φ, vehicle velocity Vx, roll angular velocity offset bro, yaw angular velocity offset bya, and top-bottom acceleration offset bzmay be estimated. The right side of Equation (19) corresponds to the function f(x,u) and the right side of Equation (20) corresponds to the function h(x,u).

An angular velocity sensor is more likely to have an offset error than an acceleration sensor. In view of this, according to the present embodiment, the offset error of the roll angular velocity sensor21(roll angular velocity sensor offset bro) and the offset error of the yaw angular velocity sensor22(yaw angular velocity sensor offset bya) are estimated. Using the estimated values of the roll angular velocity offset broand yaw angular velocity offset byain a subsequent estimation operation improves the estimation accuracy for the roll angle φ.

Further, if the offset errors of the three acceleration sensors (top-bottom acceleration sensor24, front-rear acceleration sensor25and left-right acceleration sensor26) can be estimated, the estimation accuracy for the roll angle φ is expected to further improve. However, when the offset errors of the three acceleration sensors are to be estimated, observability cannot be maintained. In view of this, according to the present embodiment, the offset error of the top-bottom acceleration sensor24(top-bottom acceleration sensor offset bz) is estimated for the following reasons. When the roll angle φ of the vehicle body1is in a small range, the top-bottom acceleration of the vehicle body1does not change significantly. If the detected value of the top-bottom acceleration in such a case is changed by the top-bottom acceleration sensor offset bz, the effect of a change in the detected value of the top-bottom acceleration when the roll angle φ is to be estimated is large. Thus, using the estimated value of the top-bottom acceleration sensor offset bzin a subsequent estimation operation further improves the estimation accuracy found when the roll angle φ is in a small range.

When the roll angle φ is to be estimated, in addition to the change in the detected value of the top-bottom acceleration, although to a lesser degree, a change in the detected value of the left-right acceleration is likely to affect the result. In view of this, as discussed above with reference toFIG. 4, the calculation unit49according to the present embodiment uses a plurality of Kalman filters50to take account of the effects of the left-right acceleration sensor offset by. Specifically, the observation equation calculation unit52of each of the Kalman filters50(50_1,50_2and50_3) receives the left-right acceleration sensor offset by(by1, by2and by3) from the imaginary offset input unit59.

The observation equation calculation unit52according to the present embodiment uses, instead of Equation (20) provided above, the following equation as the observation equation to perform calculation.

That is, each of the Kalman filters50according to the present embodiment, at the system equation calculation unit51, uses Equation (19) as the system equation to perform calculation, and, at the observation equation calculation unit52, uses Equation (21) as the observation equation to perform calculation.

The value of byin the first row of Equation (21) is the value of the left-right acceleration sensor offset by(by1,by2,by3) provided by the imaginary offset input unit59of each of the Kalman filters50(50_1,50_2and50_3).

Based on the observation equation including the left-right acceleration sensor offset by(by1,by2,by3) provided by the imaginary offset input unit59, the observation equation calculation unit52calculates the estimated values of the top-bottom acceleration Gz, left-right acceleration Gyand rear-wheel velocity vr. These estimated values are provided to the likelihood calculation unit57to calculate the value relating to the likelihood indicative of the reliability of the estimation results of the Kalman filters50(50_1,50_2and50_3).

Preferably, the largest one of the values of the left-right acceleration sensor offset by(by1,by2,by3) provided by the imaginary offset input unit59is larger than the maximum value of the zero-point offset that can be provided by the left-right acceleration sensor26, and the smallest value is smaller than the minimum value of the zero-point offset that can be provided by the left-right acceleration sensor26. Thus, the observation equation calculation unit52can calculate the estimated values of the top-bottom acceleration Gz, left-right acceleration Gyand rear-wheel velocity vrtaking account of situations where the value of the zero-point offset of the left-right acceleration sensor26is the largest.

For example, if it is ensured that the value of the zero-point offset that can be provided by the left-right acceleration sensor26is in a range of ±100 mG, then, the offset value provided by the imaginary offset input unit59of the Kalman filter50_1may be by1≥100 mG and the offset value provided by the imaginary offset input unit59of the Kalman filter50_3may be by3≤−100 mG. In this case, the offset value provided by the imaginary offset input unit59of the Kalman filter50_2may be by2=0 mG.

As discussed above, the above-described embodiment describes an implementation where the calculation unit49includes three Kalman filters50(50_1,50_2and50_3); alternatively, the value of the left-right acceleration sensor offset bymay be set in a similar way if the calculation unit49includes two Kalman filters50or four or more Kalman filters50.

Next, the likelihood λ calculated by the likelihood calculation unit57will be described. As discussed above, if the difference between an estimated value and an actual detected value is expressed by the observation/prediction error e, e is defined by the following equation.

In Equation (22), y is the detected value of the top-bottom acceleration Gz, the detected value of the left-right acceleration Gy, and the detected value of the rear-wheel velocity vrprovided by the Kalman filters50as input parameters. h(x) is the calculation result from the observation equation calculation unit52.

If the detection error in the top-bottom acceleration Gzdetected by the top-bottom acceleration sensor24is denoted by δGz, the detection error in the left-right acceleration Gydetected by the left-right acceleration sensor26is denoted by δGy, and the detection error in the rear-wheel velocity vrdetected by the rear-wheel velocity sensor7is denoted by δvr, then, the variance-covariance matrix R of these detection errors can be expressed by the equation below. In the equation below, E(a) means the expected value of a.

Further, if the estimation error in the roll angle φ is denoted by δφ, the estimation error in the vehicle velocity Vxis denoted by δVx, the estimation error in the roll angular velocity sensor offset brois denoted by δbro, the estimation error in the yaw angular velocity sensor offset byais denoted by δbya, and the estimation error in the top-bottom acceleration sensor offset bzis denoted by δbz, then, the variance-covariance matrix P of these detection errors can be expressed by the following equation.

The Kalman filters50(50_1,50_2and50_3) successively calculate the matrix P and matrix R.

The variance-covariance matrix S for the error contained in the observation/prediction error e is expressed by the following equation.
[Formula 26]
S=R+HPHt(25)

The superscript tin the equation provided above means transposed matrix. That is, Htmeans the transposed matrix of the matrix H. H is the Jacobian matrix of h(x), defined by the following equation.

The likelihood calculation unit57calculates the likelihood λ based on the equation provided below, for example. This likelihood λ is a likelihood value that is momentary, and may be called momentary likelihood.

The likelihood calculation unit57is only required to be configured to calculate the likelihood based on the observation/prediction error e=y−h(x), and the calculation method is not limited to Equation (27).

x(φ,Vx,bro,bya,bz) estimated by the Kalman filter50_1is denoted by x1, and the likelihood λ calculated by the likelihood calculation unit57of the Kalman filter50_1is denoted by λ1. Similarly, x(φ,Vx,bro,bya,bz) estimated by the Kalman filter50_2is denoted by x2, and the likelihood λ calculated by the likelihood calculation unit57of the Kalman filter50_2is denoted by λ2. Similarly, x(φ,Vx,bro,bya,bz) estimated by the Kalman filter50_3is denoted by x3, and the likelihood λ calculated by the likelihood calculation unit57of the Kalman filter50_3is denoted by λ3. Then, the left-right acceleration sensor offset byis calculated by the following equation.

In Equation 28, the character with a tilde (˜) on top of λ means a value obtained after the momentary likelihood λ has been filtered by the low-pass filter58. It does not mean that filtering is necessary. If filtering is not performed, the value of the momentary likelihood λ may be used. In Equation (28), n is the number of Kalman filters50included in the calculation unit49. According to the present embodiment, n=3.

Each Kalman filter50performs the estimation process taking account of the left-right acceleration sensor offset by(by1,by2,by3) provided by the imaginary offset input unit59. Then, the indicators that show how reliable these estimation results are are calculated, which are λ(λ1, λ2, λ3). Thus, the value of the left-right acceleration sensor offset byprovided to the imaginary offset input unit59included in each of the Kalman filters50(50_1,50_2and50_3) is weighted depending on the likelihood λ(λ1, λ2, λ3), which indicates how reliable the estimation results of the Kalman filters50(50_1,50_2and50_3) are, thereby determining the estimated value of the left-right acceleration sensor offset by.

Further, based on the same reasoning, as expressed by the equation below, x(φ,Vx,bro,bya,bz) estimated by each Kalman filter50is weighted depending on the likelihood λ(λ1, λ2, λ3), which indicates how reliable the estimation results of the Kalman filters50(50_1,50_2and50_3) are, thereby providing the estimation results taking account of the left-right acceleration sensor offset by.

The state quantity determination unit503performs the calculation represented by Equations (28) and (29). Thus, the calculation unit49estimates the left-right acceleration sensor offset by, in addition to the roll angle φ, vehicle velocity Vx, roll angular velocity offset bro, yaw angular velocity offset bya, and top-bottom acceleration offset bz. These estimated values are estimation results taking account of the left-right acceleration sensor offset by, and thus have improved accuracy over the conventional art.

Equations (28) and (29) perform calculation with proportional distribution depending on the value of the likelihood λ; however, this calculation is merely an example. The state quantity determination unit503may use any other calculation method that performs weighting depending on the value of the likelihood λ.

The attitude estimation apparatus10according to the present embodiment may use the following variations.

(1) In the embodiment described above, the state quantity determination unit503performs a calculation where the left-right acceleration sensor offset byprovided to each Kalman filter50and the estimated value x estimated by the Kalman filter are weighted depending on the likelihood λ. Alternatively, a Kalman filter50that is most likely to be reliable may be selected based on the likelihood λ calculated by the Kalman filters50, the left-right acceleration sensor offset byprovided to this Kalman filter50may be treated as the estimated value of the left-right acceleration sensor offset and the estimation results of this Kalman filter50may be treated as the estimation results of the calculation unit49. This will simplify the calculation process by the state quantity determination unit503.

(2) In the embodiment described above, each Kalman filter50estimates the roll angle φ, vehicle velocity Vx, roll angular velocity sensor offset bro, yaw angular velocity sensor offset bya, and top-bottom acceleration sensor offset bz, and the left-right acceleration sensor offset byis estimated taking account of the likelihood obtained based on the estimation results of the Kalman filters50. Alternatively, each Kalman filter50may estimate the roll angle φ, vehicle velocity Vx, roll angular velocity sensor offset bro, yaw angular velocity sensor offset bya, and left-right acceleration sensor offset by, and the top-bottom acceleration sensor offset bzmay be estimated taking account of the likelihood obtained based on the estimation results of the Kalman filters50. In this case, the top-bottom acceleration sensor24corresponds to the “detection unit of interest” or in other words, in this example the top-bottom acceleration sensor24is a detection unit associated with the offset error which is estimated by using the likelihood.

In this case, the calculation unit49has the configuration shown inFIG. 8, instead ofFIG. 4. That is, the imaginary offset input unit59is configured to provide the offset value bzfor the top-bottom acceleration sensor24to the observation equation calculation unit52. The observation equation calculation unit52uses Equation (21) as the observation equation to perform calculation. However, unlike in the above-described embodiment, the value of bz in the second row of Equation (21) is the value of the top-bottom acceleration sensor offset bz(bz1,bz2,bz3) provided by the imaginary offset input unit59of each Kalman filter50(50_1,50_2and50_3). Based on the observation equation including the top-bottom acceleration sensor offset bz(bz1,bz2,bz3) provided by this imaginary offset input unit59, the observation equation calculation unit52calculates the estimated values of the top-bottom acceleration Gz, left-right acceleration Gyand rear-wheel velocity vr. These estimated values are provided to the likelihood calculation unit57to calculate the value relating to the likelihood indicative of how reliable the estimation results of the Kalman filters50(50_1,50_2and50_3) are.

In this case, the state quantity determination unit503calculates the top-bottom acceleration sensor offset bzby performing calculation based on Equation (30), instead of Equation (28). Further, calculating the estimated value x by Equation (29) provides estimated values taking account of the roll angle φ, vehicle velocity Vx, roll angle velocity offset bro, yaw angle velocity offset bya, top-bottom acceleration offset bz, and left-right acceleration sensor offset by.

Similarly, each Kalman filter50may estimate the roll angle φ, vehicle velocity Vx, roll angle velocity sensor offset bro, yaw angle velocity sensor offset bya, and top-bottom acceleration sensor offset bz, and the front-rear acceleration sensor offset bxmay be estimated taking account of the likelihood obtained based on the estimation results of the Kalman filters50. In this case, the front-rear acceleration sensor25corresponds to the “detection unit of interest”, or in other words, in this example the front-rear acceleration sensor25is a detection unit associated with the offset error which is estimated by using the likelihood. The offset value for the front-rear acceleration sensor25provided by the imaginary offset input unit59is provided as the input parameter u for the function f(x,u) included in the system equation.

Similarly, each Kalman filter50may estimate the roll angle φ, vehicle velocity Vx, roll angle velocity sensor offset bro, yaw angle velocity sensor offset bya, and left-right acceleration sensor offset by, and the front-rear acceleration sensor offset bxmay be estimated taking account of the likelihood obtained based on the estimation results of the Kalman filters50. Again, the front-rear acceleration sensor25corresponds to the “detection unit of interest”, or in other words, in this example the front-rear acceleration sensor25is a detection unit associated with the offset error which is estimated by using the likelihood. The offset value for the front-rear acceleration sensor25provided by the imaginary offset input unit59is provided as the input parameter u for the function f(x,u) included in the system equation.

<Calculation of Estimated Values of Parameters>

Now, the ability of the attitude estimation apparatus according to the present embodiment to estimate the attitude of the vehicle100with high accuracy will be proven with reference to examples.

FIGS. 9A to 9Lare graphs showing the estimation results for the attitude of the vehicle100using the attitude estimation apparatus while the vehicle100was travelling on a predetermined road surface.FIGS. 9A to 9Lare the results of simulations made while the vehicle100traveled freely on a road surface having a horizontal surface portions and slopes. The figures show the calculation results of the estimated values of the various parameters based on the detected values of the mounted roll angle velocity sensor21, yaw angle velocity sensor22, top-bottom acceleration sensor24, front-rear acceleration sensor25, left-right acceleration sensor26, and rear-wheel velocity sensor7having no offset error when an offset error was intentionally generated at a predetermined moment in a predetermined sensor. To simulate actual situations, white noise is superimposed on the detected values from the sensors provided to the Kalman filters50.

InFIGS. 9A to 9F, the estimation results by the calculation unit49according to the present embodiment described with reference toFIG. 4are represented by solid lines. That is, the calculation unit49presupposed forFIGS. 9A to 9Fis configured such that each Kalman filter50estimates the roll angle φ, vehicle velocity Vx, roll angular velocity sensor offset bro, yaw angular velocity sensor offset bya, and top-bottom acceleration sensor offset bz, and the left-right acceleration sensor offset byis estimated taking account of the likelihood obtained based on the estimation results by the Kalman filters50.

FIGS. 9A and 9Bare graphs showing the estimated values resulting when an offset error with 250 mG was superimposed in the left-right acceleration sensor26at a predetermined moment. InFIGS. 9A and 9B, a dotted line represents a true value, a broken line represents a value estimated using one Kalman filter50, and a solid line represents a value estimated using a plurality of Kalman filters. The estimation results from the calculation unit49according to the present embodiment described with reference toFIG. 4are represented by solid lines.

InFIG. 9A, the estimated value of the left-right acceleration sensor offset byincreases after a predetermined moment, indicating that an estimation close to the true value was achieved. The estimated values of the other sensor offsets are zero, again indicating that an estimation close to the true value was achieved.

FIG. 9Bdemonstrates that the solid line achieves estimation results closer to the true value than the broken line. This shows that arranging a plurality of Kalman filters50in parallel and additionally estimating an offset error based on a likelihood improves estimation accuracy.

FIGS. 9C to 9Fare graphs showing the estimated values resulting when an offset error was generated in sensors other than the left-right acceleration sensor26. InFIGS. 9C to 9F, a dotted line represents a true value, and a solid line represents a value estimated using a plurality of Kalman filters50.

FIG. 9Cshows the estimated values found when an offset of 1 deg/sec was superimposed in the roll angular velocity sensor21after a predetermined period of time.FIG. 9Dshows the estimated values found when an offset of 1 deg/sec was superimposed in the yaw angular velocity sensor22after a predetermined period of time.FIG. 9Eshows the estimated values found when an offset of 250 mG was superimposed in the top-bottom acceleration sensor24after a predetermined period of time.

FIG. 9Fshows the estimated values found when offsets were superimposed in all the sensors in a combined manner. Specifically, an offset of 1 deg/sec was superimposed in the roll angular velocity sensor21, an offset of 1 deg/sec was superimposed in the yaw angular velocity sensor22, an offset of 250 mG was superimposed in the top-bottom acceleration sensor24, and an offset of 250 mG was superimposed in the left-right acceleration sensor26.

FIGS. 9C to 9Fdemonstrate that, immediately after an offset was superimposed, the estimated values of some other sensor offsets temporarily fluctuated, but converged over time. This verifies that, in each case, the values of the sensor offsets were estimated with high accuracy without being interfered with by other offsets.

The calculation unit49presupposed forFIGS. 9G to 9Lis configured such that each Kalman filter50estimates the roll angle φ, vehicle velocity Vx, roll angle velocity sensor offset bro, yaw angle velocity sensor offset bya, and top-bottom acceleration sensor offset bz, and the front-rear acceleration sensor offset bxwas estimated taking account of the likelihood obtained based on the estimation results of the Kalman filters50. InFIGS. 9G to 9L, the estimation results by the calculation unit49are represented by solid lines.

FIGS. 9G and 9Hare graphs showing the estimated values resulting when an offset error of 100 mG was superimposed in the front-rear acceleration sensor25at a predetermined moment. InFIGS. 9G and 9H, a dotted line represents a true value, a broken line represents a value estimated using one Kalman filter50, and a solid line represents a value estimated using a plurality of Kalman filters50.

FIG. 9Gdemonstrates that the estimated value of the front-rear acceleration sensor offset bxincreased after a predetermined moment and an estimation close to the true value was achieved. Further, the estimated values of the other sensor offsets were zero, again demonstrating that an estimation close to the true value was achieved.

FIG. 9Hdemonstrates that the solid line achieves estimation results closer to the true value than the broken line. This shows that arranging a plurality of Kalman filters50in parallel and additionally estimating an offset error based on a likelihood improves estimation accuracy.

FIGS. 9I to 9Lare graphs showing the estimation results found when an offset error was generated in sensors other than the front-rear acceleration sensor26. InFIGS. 9I to 9L, a dotted line represents a true value, and a solid line represents a value estimated using a plurality of Kalman filters50.

FIG. 9Ishows the estimated values found when an offset of 1 deg/sec was superimposed in the roll angle velocity sensor21after a predetermined period of time.FIG. 9Jshows the estimated values found when an offset of 1 deg/sec was superimposed in the yaw angular velocity sensor22after a predetermined period of time.FIG. 9Kshows the estimated values found when an offset of 100 mG was superimposed in the top-bottom acceleration sensor24after a predetermined period of time.

Further,FIG. 9Lshows the estimated values found when offsets were superimposed in all the sensors in a combined manner. Specifically, an offset of 1 deg/sec was superimposed in the roll angle velocity sensor21, an offset of 1 deg/sec was superimposed in the yaw angle velocity sensor22, an offset of 100 mG was superimposed in the top-bottom acceleration sensor24, and an offset of 100 mG was superimposed in the front-rear acceleration sensor25.

FIGS. 9I to 9Ldemonstrate that, immediately after an offset was superimposed, the estimated values of some other sensor offsets temporarily fluctuated, but converged over time. This verifies that, in each case, the values of the sensor offsets were estimated with high accuracy without being interfered with by other offsets.

Second Embodiment

A second embodiment of the present invention will be described. The vehicle body100is the same as in the first embodiment and will not be described again.

<Configuration of Attitude Estimation Apparatus>

FIG. 10is a schematic block diagram of an exemplary configuration of the attitude estimation apparatus according to the present embodiment. The attitude estimation apparatus10shown inFIG. 10includes a group of sensors5, a rear-wheel velocity sensor7, low-pass filters31,32,33,34,35and36, differentiators41,42,43and45, a calculation unit49, a load estimation unit60, a suspension stroke quantity estimation unit70, an elevation/depression angle estimation unit80, and a slope estimation unit90. The functionality of the calculation unit49, load estimation unit60, suspension stroke quantity estimation unit70, elevation/depression angle estimation unit80and slope estimation unit90is implemented by the ECU20shown inFIG. 2operating in accordance with a program. The calculation unit49corresponds to the “attitude estimation unit”.

According to the present embodiment, the group of sensors5includes a roll angular velocity sensor21, a yaw angular velocity sensor22, a pitch angular velocity sensor23, a top-bottom acceleration sensor24, a front-rear acceleration sensor25, and a left-right acceleration sensor26. That is, the present embodiment is different from the first embodiment in that the group of sensors5further includes a pitch angular velocity sensor23. The pitch angular velocity sensor23is provided on the vehicle body1so as to detect the pitch angular velocity of the vehicle body1, and corresponds to the “third angular velocity detection unit”.

The output signal from the pitch angular velocity sensor23is passed through the low-pass filter33and is fed into the calculation unit49and differentiator43as the pitch angular velocity. The low-pass filter33removes noise from the output signal from the pitch angular velocity sensor23. The differentiator43provides, as a pitch angular acceleration, the differential value of the pitch angular velocity to the calculation unit49.

The frequency characteristics of the low-pass filter33are decided depending on the output characteristics of the pitch angular velocity sensor23. More specifically, the frequency characteristics of the noise components contained in the output signal from the pitch angular velocity sensor23can be identified in advance, at the stage of apparatus design. The low-pass filter33may be designed to block these noise components and pass the detection signal of the pitch angular velocity sensor23.

In the present embodiment, the calculation unit49receives detected values relating to the roll angular velocity, roll angular acceleration, yaw acceleration, yaw angular acceleration, pitch angular velocity, pitch angular acceleration, top-bottom acceleration, front-rear acceleration, left-right acceleration, and rear-wheel velocity. Then, based on these values, the calculation unit49estimates the roll angle, pitch angle, front-rear direction vehicle velocity, roll angular velocity sensor offset, yaw angular velocity sensor offset, pitch angular velocity sensor offset, top-bottom acceleration sensor offset, and front-rear acceleration sensor offset, and output them. The pitch angular velocity sensor offset is the offset error for the pitch angular velocity sensor23.

The attitude estimation apparatus10according to the present embodiment includes an elevation/depression angle estimation unit80, and is configured such that the calculation unit49also receives an estimated value of the elevation/depression angle from the elevation/depression angle estimation unit80.

The pitch angular velocity sensor offset estimated by the calculation unit49, the elevation/depression angle estimated by the elevation/depression angle estimation unit80, and the first-order differential value of the elevation/depression angle will be denoted by the following characters.

FIG. 11illustrates the elevation/depression angle ζ. The elevation/depression angle ζ is defined as the angle formed by the road-surface coordinate system erand vehicle coordinate system ebfound when a stroke quantity of the suspension of the vehicle100is present. In the following description, the elevation/depression angle ζ is the angle formed by the x-axis ebxof the vehicle coordinate system eband the x-axis erxof the road-surface coordinate system.

FIG. 12is a schematic block diagram of an exemplary configuration of the calculation unit49according to the present embodiment. According to the present embodiment, similar to the first embodiment described with reference toFIG. 4, the calculation unit49includes a plurality of Kalman filters50_1,50_2and50_3and a state quantity determination unit503.FIG. 12shows an implementation where the calculation unit49includes three Kalman filters50; however, this number is merely an example. The calculation unit49may include two Kalman filters50, or four or more Kalman filters50.

The Kalman filters50(50_1,50_2and50_3) use a kinematic model for the vehicle100, described below. Similar toFIG. 4, inFIG. 12, the Kalman filters50(50_1,50_2and50_3) each include a system equation calculation unit51, an observation equation calculation unit52, a subtractor53, an adder54, an integrator55, a Kalman gain calculation unit56, a likelihood calculation unit57, a low-pass filter58, and an imaginary offset input unit59.

The system equation that is an equation used for the calculation at the system equation calculation unit51includes the function f(x,u). The observation equation that is an equation used for the calculation at the observation equation calculation unit52includes the function h(x,u). The Kalman gain calculation unit56includes a seventh-degree Kalman gain K.

The imaginary offset input unit59provides, as an offset value for a predetermined one of the sensors of the group5, a predetermined value for each of the Kalman filters50(50_1,50_2and50_3) to the system equation calculation unit51and observation equation calculation unit52. According to the present embodiment, the imaginary offset input unit59is configured to provide the offset value bxfor the front-rear acceleration sensor52to the system equation calculation unit51and observation equation calculation unit52. In this implementation, the front-rear acceleration sensor25corresponds to the “detection unit of interest”, or in other words, in this example the front-rear acceleration sensor25is a detection unit associated with the offset error whose imaginary offset quantity is provided in each of the Kalman filters50(50_1,50_2and50_3).

InFIG. 12, the offset value for the front-rear acceleration sensor25provided by the imaginary offset input unit59of the Kalman filter50_1to the system equation calculation unit51and observation equation calculation unit52is denoted by bx1. Similarly, the offset value for the front-rear acceleration sensor25provided by the imaginary offset input unit59of the Kalman filter50_2to the system equation calculation unit51and observation equation calculation unit52is denoted by bx2. The offset value for the front-rear acceleration sensor25provided by the imaginary offset input unit59of the Kalman filter50_3to the system equation calculation unit51and observation equation calculation unit52is denoted by bx3.

The likelihood calculation unit57determines a likelihood using calculation, the likelihood being indicative of the reliability of the result from the estimation by the Kalman filter50(50_1,50_2or50_3). The low-pass filter58is a calculation unit for filtering the value calculated by the likelihood calculation unit57. The low-pass filter58may be replaced by any other calculation unit that can implement the same functionality. It is optional whether the low-pass filter58is included. The calculation performed by the likelihood calculation unit57will be described further below.

The calculation units51,52,53,54,55,56,57and58constituting a Kalman filter50may be implemented by, for example, a program that has been prepared in advance being performed by the ECU20. Alternatively, some or all of the calculation units51,52,53,54,55,56,57and58may be implemented by independent hardware mounted.

According to the present embodiment, in the current estimation operation, the following values are provided as the input parameter u of the function f(x,u) included in the system equation: the detected value of the roll angular velocity ωro, the detected value of the roll angular acceleration (differential value of the roll angular velocity ωro), the detected value of the yaw angular velocity ωya, the yaw angular acceleration (differential value of the yaw angular velocity ωya), the detected value of the pitch angular velocity ωpi, the detected value of the pitch angular acceleration (differential value of the pitch angular velocity ωpi) and the detected value of the front-rear acceleration Gx.

Further, the following values are provided as the input parameter x of the function f(x,u) included in the system equation: the estimated value of the roll angle φ, the estimated value of the pitch angle θ, the estimated value of the vehicle velocity Vx, the estimated value of the roll angular velocity sensor offset bro, the estimated value of the yaw angular velocity sensor offset bya, the estimated value of the pitch angular velocity sensor offset bpi, and the estimated value of the top-bottom acceleration sensor offset bzfrom the previous estimation operation. Further, as discussed above, the offset value for the front-rear acceleration sensor25provided by the imaginary offset input unit59is provided as the input parameter u of the function f (x,u) included in the system equation.

The output from the system equation calculation unit51is the predicted differential value of the roll angle φ, the predicted differential value of the pitch angle θ, the predicted differential value of the vehicle velocity Vx, the predicted differential value of the roll angular velocity sensor offset bro, the predicted differential value of the yaw angular velocity sensor offset bya, the predicted differential value of the pitch angular velocity sensor offset bpi, and the predicted differential value of the top-bottom acceleration sensor offset bz.

The adder54adds the seventh-degree Kalman gain K obtained by the previous estimation operation to the predicted differential value of the roll angle φ, the predicted differential value of the pitch angle θ, the predicted differential value of the vehicle velocity Vx, the predicted differential value of the roll angular velocity sensor offset bro, the predicted differential value of the yaw angular velocity sensor offset bya, the predicted differential value of the pitch angular velocity sensor offset bpi, and the predicted differential value of the top-bottom acceleration sensor offset bz.

The predicted differential value of the roll angle φ, the predicted differential value of the pitch angle θ, the predicted differential value of the vehicle velocity Vx, the predicted differential value of the roll angular velocity sensor offset bro, the predicted differential value of the yaw angular velocity sensor offset bya, the predicted differential value of the pitch angular velocity sensor offset bpi, and the predicted differential value of the top-bottom acceleration sensor offset bzto which the Kalman gain K has been added are integrated by the integrator55. This provides the estimated value of the roll angle φ, the estimated value of the pitch angle θ, the estimated value of the vehicle velocity Vx, the estimated value of the roll angular velocity sensor offset bro, the estimated value of the yaw angular velocity sensor offset bya, the estimated value of the pitch angular velocity sensor offset bpi, and the estimated value of the top-bottom acceleration sensor offset bzfrom the current estimation operation.

The following values are provided as the input parameter x of the function h(x,u) included in the observation equation: the estimated value of the roll angle φ, the estimated value of the pitch angle θ, the estimated value of the vehicle velocity Vx, the estimated value of the roll angular velocity sensor offset bro, the estimated value of the yaw angular velocity sensor offset bya, the estimated value of the pitch angular velocity sensor offset bpi, and the estimated value of the top-bottom acceleration sensor offset bz. Further, as discussed above, the offset value for front-rear acceleration sensor25provided by the imaginary offset input unit59is provided as the input parameter x of the function h(x,u) included in the observation equation.

The output from the observation equation calculation unit52is the calculated value of the top-bottom acceleration Gz, the calculated value of the left-right acceleration Gy, the calculated value of the rear-wheel velocity vr, and the calculated value of the characteristic equation discussed below.

The Kalman filters50each receive, as the input parameter y, the detected value of the top-bottom acceleration Gz, the detected value of the left-right acceleration Gy, the detected value of the rear-wheel velocity vrand a constant that serves as the detected value of the characteristic equation. The Kalman gain calculation unit56calculates the Kalman gain K based on the difference between the detected values of the top-bottom acceleration Gz, left-right acceleration Gy, rear-wheel velocity vrand the detected value of the characteristic equation, and the calculated values. In the description further below, the detected value of the characteristic equation is zero; however, it is not limited to zero and may be any constant that does not change over time.

Finding this kinematic model's system equation f(x,u) and observation equation h(x,u) gives the relational expression for the input parameters u and y and the output parameter x.

<Finding of System Equation and Observation Equation>

The present embodiment supposes the following points to simplify the kinematic model.

(a) There is no rotational slide between the rear wheel3and road surface.

(b) The side-skid velocity of the rear wheel3is zero.

(c) The road surface is flat. “Flat” as used herein means a plane without irregularities, and may be inclined.

Based on suppositions (a) to (c), the kinematic model equation is found in the following manner. That is, it is different from the first embodiment in that the finding of the kinematic model does not incorporate supposition (d).

Based on Equation (7) provided above, the second-order differential vector of the vector ρ is determined by the equation provided below. It is different from Equation (8) in that the second-order differential vector of the vector ρ takes account of the pitch angular velocity ωpiand its first-order differential value.

In Equation (31), ax, ayand azare functions. The functions ax, ayand azmay be determined by calculating Equations (6) and (7) provided above. Using Equation (1) to rearrange Equation (6) into Equation (7) removes the differential value of the roll angle φ, the differential value of the yaw angle ψ and the differential value of the pitch angle θ.

Next, the side-skid velocity of the vehicle100is denoted by Vy. The first-order differential vector of the positional vector r0inFIG. 7is expressed by the equation below using the vehicle velocity Vxand side-skid velocity Vy. As discussed above, the vector ebis the basis vector of the vehicle coordinate system. According to the present embodiment, the elevation/depression angle caused by the slope of the road surface200and the stroke of the suspension is also taken into consideration for estimation, and thus the velocity Vzfor the z-direction of the vehicle100is generated. That is, the first-order differential vector of the positional vector r0inFIG. 7is expressed by the following equation using Vx, Vyand Vz.

From Supposition (b), Vy=0; when Equation (32) is first-order differentiated, the second-order differential vector of the positional vector r is expressed by the equation provided below. The present embodiment is different from the first embodiment in that the pitching of the vehicle100is taken into consideration, and thus the equation is different from Equation (10) provided above.

As discussed above, the elevation/depression angle ζ is the angle formed by the x-axis of the vehicle coordinate system eband the x-axis of the road-surface coordinate system er. This definition gives the following equation.
[Formula 35]
Vz=Vxtan ζ  (34)

Thus, Equation (33) may be rewritten into the following equation, Equation (35).

Next, the gravity acceleration vector may be expressed by the equation below. g in the right side of Equation (36) below indicates the magnitude of the gravity acceleration.

Based on Equations (5), (31), (35) and (36), the acceleration vector G detected at the mounting position PS may be expressed by the following equation.

The acceleration vector G detected at the mounting position PS may be expressed by Equation (13) provided above using the front-rear acceleration Gxdetected by the front-rear acceleration sensor25, the left-right acceleration Gydetected by the left-right acceleration sensor26, and the top-bottom acceleration Gzdetected by the top-bottom acceleration sensor24. Thus, based on Equations (13) and (37), the front-rear acceleration Gx, left-right acceleration Gyand top-bottom acceleration Gzmay be expressed by the following equation.

Next, the relationship between the rear-wheel velocity vrand vehicle velocity Vxis determined. Supposition (a) means that there is no slide between the rear wheel3and road surface200, and thus the relationship expressed by the following equation can be established between the rear-wheel velocity vrand vehicle velocity Vx.

Equation (39) gives the following equation.

Equations (1), (38) and (40) give the following equation.

As shown inFIG. 7, the vector from the mounting position PS for the group of sensors5to the gravity point MP of the main body of the vehicle body1is denoted by W. The gravity point MP does not take account of the weight of those portions of the vehicle body1that are located below the spring. The weight of the portions of the vehicle body1that are located below the spring are the total weight of those parts of the vehicle body1that are closer to the road surface than the suspension as determined along the routes of transfer of forces from the road surface. The parts closer to the road surface than the suspension include, for example, the tires2and3, the wheels, the brakes etc. The vector W is expressed by the following equation.

Since the weight of the vehicle body1is generally distributed symmetrically with respect to the vertical centerline, Wy=0. If the mass of the main body of the vehicle body1is denoted by M and the inertia moment tensor is denoted by I, the angular momentum vector L of the vehicle100as viewed from the reference point O is calculated in the manner described below. In the following vector calculation equation, “●” means an inner product and “×” means an outer product.

In Equation (43), the angular velocity vector ω and tensor I are defined by the following equations.

In Equation (45), an operator with a circle including a cross means a “tensor product”.

Temporally differentiating both sides of Equation (43) gives the following equation.

If it is rearranged in a manner similar to that for the rearrangement of Equation (7) into Equation (31), the second-order differential vector of the vector W and the inner product of the tensor I and angular acceleration vector ω dot (first-order differential vector of the angular velocity vector ω) are expressed by the following equations.

wx, wyand wzin Equation (47) and qx, qyand qzin Equation (48) are functions. These functions may be calculated by a rearrangement similar to the rearrangement of Equation (7) to Equation (31).

Next, the moments of forces acting on the vehicle100as viewed from the reference point O are calculated.FIG. 13shows the vectors of forces acting on the road-surface grounding points of the front and rear wheels in a manner similar to that ofFIG. 7. As shown inFIG. 13, forces acting on the vehicle100include the gravity vector −Mg, the aerodynamic vector γ composed of the air resistance, lift force etc., the force vector FF acting on the road-surface grounding point P2for the front wheel2, and the force vector FR acting on the road-surface grounding point P3for the rear wheel3. The force vector FF acting on the road-surface grounding point P2for the front wheel2and the force vector FR acting on the road-surface grounding point P3for the rear wheel3are expressed by the following equations.

The force of gravity and the aerodynamic force act on the gravity point MP of the vehicle100. The aerodynamic vector γ is expressed by the following equation.

The aerodynamic force depends on the velocity V and angular velocity ω of the vehicle100, and thus may be expressed by the following equation.

In Equation (51), the rearrangement is done using Vy=0, which is based on Supposition (b), and Equation (34).

As shown inFIG. 13, if the vector from the mounting position PS for the group of sensors5to the road-surface grounding point P2for the front wheel2is denoted by σ, the vector σ is expressed by the equation provided below. In the following equation, the horizontal distance from the mounting position PS to the center of the front wheel2is denoted by L′.

The moment vector N of the force acting on the vehicle100as viewed from the reference point O is expressed by the following equation.

The quantity of the first-order differential of the angular momentum vector L of the vehicle100as viewed from the reference point O is the same as the moment vector N of the force acting on the vehicle100as viewed from the reference point O. That is, the following equation is established.

Equation (54) should be true regardless of where the reference point is located. That is, Equation (54) should be true for any positional vector r. Thus, if Equations (46) and (53) are identical equations, the following two equations are established.
[Formula 56]
M({umlaut over (r)}0+{umlaut over (ρ)}+{umlaut over (W)})=−Mg+γ+FF+FR(55)
[Formula 57]
M(ρ+W)×({umlaut over (r)}0+{umlaut over (ρ)}+{umlaut over (W)})+I·{dot over (ω)}=(ρ+W)×(−Mg+γ)+(ρ+σ)×FF(56)

Expressing Equation (57) using entries gives Equation (58) below.

Using Equation (59), Equation (58) may be rewritten into the following equation.

Using Equation (55), Equation (56) may be rearranged into the following equation.

Expressing Equation (61) using entries gives the following equation, Equation (64).

Substituting the second and third entries of Equation (60) for the first entry of Equation (64) removes FFy, FRy, FFzand FRz. That is, Equation (65) below is given.

The factors of the function Qxof Equation (66) are all numbers that can be uniquely derived from the vehicle data, for example, except the values provided by the group of sensors5and the values estimated by the attitude estimation apparatus10. That is, Equation (66) means that the function Qx, indicating a constant, necessarily exists regardless of time. An equation satisfying this function “Qx=constant” corresponds to the “characteristic equation”.

Based on Equation (59), Equation (41) may be rewritten into the following equation.

Further, from Equations (38), (40) and (66), the following equation may be derived.

Each of the Kalman filters50(50_1,50_2and50_3), at the system equation calculation unit51, uses Equation (67) as the system equation to perform calculation, and, at the observation equation calculation unit52, uses Equation (68) as the observation equation to perform calculation.

Even if the roll angular velocity offset bro, yaw angular velocity offset bya, pitch angular velocity offset bpiand top-bottom acceleration offset bzvary, such variances are small compared with movements of the vehicle100. Thus, the differential value of the roll angular velocity offset bro, the differential value of the yaw angular velocity offset bya, the differential value of the pitch angular velocity offset bpi, and the differential value of the top-bottom acceleration offset bzcan be considered to be zero.

Further, replacing some values in Equations (67) and (68) with values reflecting offset errors gives the following equations.

According to the present embodiment, as discussed above with reference toFIG. 12, the system equation calculation unit51and observation equation calculation unit52of each of the Kalman filters50(50_1,50_2and50_3) receive the front-rear acceleration sensor offset bx(bx1,bx2,bx3) from the imaginary offset input unit59. That is, the system equation calculation unit51according to the present embodiment performs calculation using Equation (69a) provided below, instead of Equation (69), as the system equation, and the observation equation calculation unit52performs calculation using Equation (70a) provided below, instead of Equation (70), as the observation equation.

That is, each of the Kalman filters50according to the present embodiment, at the system equation calculation unit51, uses Equation (69a) as the system equation to perform calculation, and, at the observation equation calculation unit52, uses Equation (70a) as the observation equation to perform calculation.

The value of bxin Equations (69a) and (70a) is the value of the front-rear acceleration sensor offset bx(bx1,bx2,bx3) provided by the imaginary offset input unit59of each of the Kalman filters50(50_1,50_2and50_3).

Based on the system equation including the front-rear acceleration sensor offset bx(bx1,bx2,bx3) provided by the imaginary offset input unit59, the system equation calculation unit51calculates the estimated values of the roll angle, pitch angle θ, vehicle velocity Vx, roll angular velocity sensor offset bro, yaw angular velocity sensor offset bya, pitch angular velocity sensor offset bpi, and top-bottom acceleration sensor offset bz. Similarly, based on the observation equation including the front-rear acceleration sensor offset bx(bx1,bx2,bx3) provided by the imaginary offset input unit59, and using also the estimation results from the system equation calculation unit51, the observation equation calculation unit52calculates the estimated values of the top-bottom acceleration Gz, left-right acceleration Gyand rear-wheel velocity vr. These estimated values are provided to the likelihood calculation unit57to calculate the value relating to the likelihood indicative of the reliability of the estimation results of the Kalman filters50(50_1,50_2and50_3).

Preferably, the largest one of the values of the front-rear acceleration sensor offset bx(bx1,bx2,bx3) provided by the imaginary offset input unit59is larger than the maximum value of the zero-point offset that can be provided by the front-rear acceleration sensor25, and the smallest value is smaller than the minimum value of the zero-point offset that can be provided by the front-rear acceleration sensor25. Thus, the observation equation calculation unit52can calculate the estimated values of the top-bottom acceleration Gz, left-right acceleration Gyand rear-wheel velocity vrtaking account of situations where the value of the zero-point offset of the front-rear acceleration sensor25is the largest.

As discussed above, the above-described embodiment describes an implementation where the calculation unit49includes three Kalman filters50(50_1,50_2and50_3); alternatively, the value of the front-rear acceleration sensor offset bxmay be set in a similar way if the calculation unit49includes two Kalman filters50or four or more Kalman filters50.

Next, the likelihood λ calculated by the likelihood calculation unit57will be described. If the detection error in the top-bottom acceleration Gzdetected by the top-bottom acceleration sensor24is denoted by δGz, the detection error in the left-right acceleration Gydetected by the left-right acceleration sensor26is denoted by δGy, the detection error in the rear-wheel velocity vrdetected by the rear-wheel velocity sensor7is denoted by δvr, and the model equation error in the rotational-motion equation about the roll axis is denoted by δQx, then, the variance-covariance matrix R of these detection errors can be expressed by the equation below. As discussed above, the motion-equation model is created by supposing the weight of the rider and treating the rider as a rigid body, and this causes errors derived from these suppositions. This error is represented by δQx.

Further, if the estimation error in the roll angle φ is denoted by δφ, the estimation error in the pitch angle θ is denoted by δθ, the estimation error in the vehicle velocity Vxis denoted by δVx, the estimation error in the roll angular velocity sensor offset brois denoted by δbro, the estimation error in the yaw angular velocity sensor offset byais denoted by δbya, the estimation error in the pitch angular velocity sensor offset bpiis denoted by δbpi, and the estimation error in the top-bottom acceleration sensor offset bzis denoted by δbz, then, the variance-covariance matrix P of these detection errors can be expressed by the following equation.

The Kalman filters50(50_1,50_2and50_3) successively calculate the matrix P and matrix R.

The variance-covariance matrix S for the error contained in the observation/prediction error e is expressed by Equation (25) provided above. The following equation is the same as provided above.
[Formula 74]
S=R+HPHt(25)

In Equation (25), H is the Jacobian matrix of h(x), defined by the following equation.

Based on the matrix H defined by Equation (74), the likelihood calculation unit57calculates the likelihood λ based on, for example, Equation (27), as in the first embodiment. Based on the likelihood λ provided by each of the Kalman filters50(50_1,50_2and50_3), the state quantity determination unit503weights the value of the front-rear acceleration sensor offset bxto calculate the estimated value of the front-rear acceleration sensor offset bx. Based on the same reasoning, x(φ, θ, Vx,bro,bya,bz) estimated by each Kalman filter50is weighted depending on the likelihood λ(λ1, λ2, λ3), which indicates how reliable the estimation results of the Kalman filters50(50_1,50_2and50_3) are, thereby providing the estimation results taking account of the front-rear acceleration sensor offset bx.

<Configuration of Load Estimation Unit>

The load estimation unit60performs the calculation described above based on the result of the calculation at the calculation unit49to calculate the force FP acting on the front wheel2and the force FR acting on the rear wheel3.

<Configuration of Suspension Stroke Quantity Estimation Unit>

The suspension stroke quantity estimation unit70performs calculation based on the estimation results from the load estimation unit60to estimate the stroke quantity of the suspension provided on at least one of the front wheel2and rear wheel3.

If the caster angle is denoted by εF, the force Ffacting on the front fork16is calculated by FF2cos εF−FFxsin εF. The caster angle is the angle formed by the direction extending vertically upward from the grounding point P2of the front wheel3and the front fork16.

The stroke quantity of the shock absorber of the rear suspension is denoted by δR, and the displacement of the center of the rear wheel3is denoted by δW. The hang angle of the rear arm relative to the horizontal direction found when the stroke quantity of the shock absorber of the rear suspension is δRis denoted by εR(δR). The caster angle εFand the rear arm angle εR(δW) are shown inFIG. 14.

Then, the force F1acting on the suspension is expressed by the following equation.

In Equation (79), κ is data called lever ratio which may be calculated from design drawings.FIG. 15shows an example of the relationship between δRand δW.

The suspension stroke quantity estimation unit 70 performs the following estimation processing.

Then, the force Ffacting on the front fork and the force F1acting on the suspension may be calculated by the following Equations (82) and (83).

FIG. 16is an equivalent circuit modelling the shock absorber of the suspension. The suspension may be represented by a parallel model with a spring k and a damper c as shown inFIG. 16in an equivalent manner. Creating a motion equation based on this equivalent circuit gives the equation provided below. The stroke quantity of the shock absorber of each suspension, the spring constant of each shock absorber, the damper coefficient of each shock absorber, and the preset load applied to each spring will be denoted by the characters shown in the following table.

TABLE 7ParameterMeaningδFstroke quantity of shock absorber of front-wheel suspensionδRstroke quantity of shock absorber of rear-wheel suspensionkFspring coefficient of shock absorber offront-wheel suspensionkRspring coefficient of shock absorber ofrear-wheel suspensioncFattenuation coefficient of shock absorber offront-wheel suspensioncRattenuation coefficient of shock absorber ofrear-wheel suspensionlFpreset load applied to shock absorber offront-wheel suspensionlRpreset load applied to shock absorber ofrear-wheel suspension

In Equations (84) and (85), the parameters in the function DFin Equation (82) and the function DRin Equation (83) are omitted.

Solving the differential equation of Equations (84) and (85) gives the following equation.

As discussed above, the suspension stroke quantity estimation unit70may calculate the stroke quantity of the shock absorber of the suspension by solving the differential equations in Equations (84) and (85), or calculate the stroke quantity of the shock absorber of the suspension by performing a discretization process over time.

Further, the suspension stroke quantity estimation unit70may calculate the amount of sink δWof the rear suspension by performing calculation based on the following equation.
[Formula 83]
δW=∫0δRκ(δ)dδ(87)

The elevation/depression angle estimation unit80calculates the elevation/depression angle ζ based on the estimation results from the load estimation unit60and suspension stroke quantity estimation unit70.

If the suspension of the front wheel sinks by the amount of sink δFand the suspension of the rear wheel sinks by the amount of sink δW, the positional vector Foof the center of the front wheel and the positional vector Roof the center of the rear wheel may be expressed by the following equations.

Based on Equations (87) and (88), the elevation/depression angle ζ may be calculated by the following equation.

In Equation (89), ζ0is the angle formed by the basis vector ebof the vehicle coordinate system and the basis vector erof the road-surface coordinate system found when the stroke quantity of the suspension is zero.

<Configuration of Slope Estimation Unit>

Based on the estimation results from the elevation/depression angle estimation unit80and the estimation results from the calculation unit49, the slope estimation unit90calculates the longitudinal slope η of the road surface.

The longitudinal slope η of the road surface is expressed by the following equation using the pitch angle θ, roll angle φ and elevation/depression angle ζ of the vehicle100.
[Formula 86]
sin η=sin θ cos ζ−cos θ cos ϕ sin ζ  (90)

Thus, the longitudinal slope η of the road surface may be calculated by the following equation.
[Formula 87]
η=sin−1(sin θ cos ζ−cos θ cos ϕ sin ζ)   (91)

The attitude estimation apparatus10according to the present embodiment may use one or more of the following variations.

(1) As in the first embodiment, according to the present embodiment, a Kalman filter50that is most likely to be reliable may be selected based on the likelihood λ calculated by the Kalman filters50, the left-right acceleration sensor offset byprovided to this Kalman filter50may be treated as the estimated value of the left-right acceleration sensor offset and the estimation results of this Kalman filter50may be treated as the estimation results of the calculation unit49. This will simplify the calculation process by the state quantity determination unit503.

(2) According to the present embodiment, each Kalman filter50estimates the roll angle φ, pitch angle θ, vehicle velocity Vxroll angular velocity sensor offset bro, yaw angle velocity sensor offset bya, pitch angular velocity sensor offset bpi, and top-bottom acceleration sensor offset bz, and the front-rear acceleration sensor offset bxis estimated taking account of the likelihood obtained based on the estimate results of the Kalman filters50. Alternatively, each Kalman filter50may estimate the roll angle φ, vehicle velocity Vx, roll angular velocity sensor offset bro, yaw angle velocity sensor offset bya, pitch angular velocity sensor offset bpi, and front-rear acceleration sensor offset bx, and the top-bottom acceleration sensor offset bzmay be estimated taking account of the likelihood obtained based on the estimation results of the Kalman filters50. In this case, the top-bottom acceleration sensor24corresponds to the “detection unit of interest”, or in other words, in this example the top-bottom acceleration sensor24is a detection unit associated with the offset error which is estimated by using the likelihood.

In this case, each of the Kalman filters50included in the calculation unit49according to the present embodiment, at the system equation calculation unit51, performs calculation using Equation (92) provided below, instead of Equation (69a), as the system equation. Further, each of the Kalman filters50, at the observation equation calculation unit52, performs calculation using Equation (93) provided below, instead of Equation (70a), as the observation equation.

The value of bzin Equation (93) is the value of the top-bottom acceleration sensor offset bz(bz1,bz2,bz3) provided by the imaginary offset input unit59of each of the Kalman filters50(50_1,50_2and50_3).

Alternatively, the left-right acceleration sensor offset bymay be estimated taking account of the likelihood obtained based on the estimation results from the Kalman filters50. In this case, each of the Kalman filters50, at the system equation calculation unit51, performs calculation using Equation (94) provided below, instead of Equation (69a), as the system equation, and, at the observation equation calculation unit52, performs calculation using Equation (95) provided below, instead of Equation (70a), as the observation equation.

Similarly, the value of byin Equation (95) may be the value of the left-right acceleration sensor offset by(by1,by2,by3) provided by the imaginary offset input unit59of each of the Kalman filters50(50_1,50_2and50_3).

(3) According to the present embodiment, the attitude estimation apparatus10includes the load estimation unit60, suspension stroke quantity estimation unit70, elevation/depression angle estimation unit80, and slope estimation unit90. Alternatively, the attitude estimation apparatus10may include none of these calculation functionalities. That is, as shown inFIG. 17, the attitude estimation apparatus10may include the group of sensors5, rear-wheel velocity sensor7, low-pass filters31,32,33,34,35and36, and differentiators41,42and43, and calculation unit49.

In this case, unlike the arrangement ofFIG. 10, the attitude estimation apparatus10does not include the functionality for estimating the elevation/depression angle ζ, and thus the calculation unit49does not receive the elevation/depression angle ζ and the first-order differential value of the elevation/depression angle ζ. Accordingly, the calculation unit49may perform calculation where ζ=0 in the equations provided above. In this arrangement, the vehicle100is capable of estimating the attitude of the vehicle body1without taking account of movements of the suspension.

<Calculation of Estimated Values of Parameters>

Now, the ability of the attitude estimation apparatus according to the present embodiment to estimate the attitude of the vehicle100with high accuracy will be proven with reference to examples. For the examples provided below, similar to the first embodiment, the attitude of the vehicle100was estimated by the attitude estimation apparatus according to the present embodiment while the value100traveled on a predetermined road surface. During this, an offset errors was intentionally generated in a predetermined sensor at a predetermined moment.

The calculation unit49presupposed forFIGS. 18A to 18Gis configured such that each Kalman filter50estimates the roll angle φ, vehicle velocity Vx, roll angular velocity sensor offset bro, yaw angular velocity sensor offset bya, pitch angular velocity sensor offset bpi, and top-bottom acceleration sensor offset bz, and the front-rear acceleration sensor offset bxis estimated taking account of the likelihood obtained based on the estimation results by the Kalman filters50.

FIGS. 18A and 18Bare graphs showing the estimated values resulting when an offset error with 100 mG was superimposed in the front-rear acceleration sensor25at a predetermined moment. InFIGS. 18A and 18B, a dotted line represents a true value, a broken line represents a value estimated using one Kalman filter50, and a solid line represents a value estimated using a plurality of Kalman filters50. The data represented by the broken line is data obtained when the value of one sensor offset was additionally estimated using the characteristic equation, as discussed in the present embodiment.

InFIG. 18A, the estimated value of the front-rear acceleration sensor offset bxincreases after a predetermined moment, indicating that an estimation close to the true value was achieved. The estimated values of the other sensor offsets are zero, again indicating that an estimation close to the true value was achieved. ForFIG. 18A, the data represented by the broken line was not obtained through an estimation process using a likelihood, and this arrangement does not allow the front-rear acceleration sensor offset bxto be estimated.

FIG. 18Bdemonstrates that the solid line achieves estimation results closer to the true value than the broken line. This shows that arranging a plurality of Kalman filters50in parallel and additionally estimating an offset error based on a likelihood improves estimation accuracy.

FIGS. 18C to 18Gare graphs showing the estimated values resulting when an offset error was generated in sensors other than the front-rear acceleration sensor25. InFIGS. 18C to 18G, a dotted line represents a true value, and a solid line represents a value estimated using a plurality of Kalman filters50.

FIG. 18Cshows the estimated values found when an offset of 1 deg/sec was superimposed in the roll angular velocity sensor21after a predetermined period of time.FIG. 18Dshows the estimated values found when an offset of 1 deg/sec was superimposed in the pitch angular velocity sensor23after a predetermined period of time.FIG. 18Eshows the estimated values found when an offset of 1 deg/sec was superimposed in the yaw angular velocity sensor22after a predetermined period of time.FIG. 18Fshows the estimated values found when an offset of 100 mG was superimposed in the top-bottom acceleration sensor24after a predetermined period of time.

FIG. 18Gshows the estimated values found when offsets were superimposed in all the sensors in a combined manner. Specifically, an offset of 1 deg/sec was superimposed in the roll angular velocity sensor21, an offset of 1 deg/sec was superimposed in the pitch angular velocity sensor23, an offset of 1 deg/sec was superimposed in the yaw angular velocity sensor22, an offset of 100 mG was superimposed in the top-bottom acceleration sensor24, and an offset of 100 mG was superimposed in the front-rear acceleration sensor25.

FIGS. 18C to 18Gdemonstrate that, immediately after an offset was superimposed, the estimated values of some other sensor offsets temporarily fluctuated, but converged over time. This verifies that, in each case, the values of the sensor offsets were estimated with high accuracy without being interfered with by other offsets.

The calculation unit49presupposed forFIGS. 19A to 19Gis configured such that each Kalman filter50estimates the roll angle φ, vehicle velocity Vx, roll angular velocity sensor offset bro, yaw angular velocity sensor offset bya, pitch angular velocity sensor offset bpi, and front-rear acceleration sensor offset bx, and the top-bottom acceleration sensor offset bzis estimated taking account of the likelihood obtained based on the estimation results by the Kalman filters50.

FIGS. 19A and 19Bare graphs showing the estimated values resulting when an offset error with 100 mG was superimposed in the front-rear acceleration sensor24at a predetermined moment. InFIGS. 19A and 19B, a dotted line represents a true value, a broken line represents a value estimated using one Kalman filter50, and a solid line represents a value estimated using a plurality of Kalman filters50. The data represented by the broken line is data obtained when the value of one sensor offset was additionally estimated using the characteristic equation, as discussed in the present embodiment.

InFIG. 19A, the estimated value of the top-bottom acceleration sensor offset bzincreases after a predetermined moment, indicating that an estimation close to the true value was achieved. The estimated values of the other sensor offsets are zero, again indicating that an estimation close to the true value was achieved. ForFIG. 19A, the data represented by the broken line was not obtained through an estimation process using a likelihood, and this arrangement does not allow the front-rear acceleration sensor offset bzto be estimated.

FIG. 19Bdemonstrates that the solid line achieves estimation results closer to the true value than the broken line. This shows that arranging a plurality of Kalman filters50in parallel and additionally estimating an offset error based on a likelihood improves estimation accuracy.

FIGS. 19C to 19Gare graphs showing the estimated values resulting when an offset error was generated in sensors other than the front-rear acceleration sensor24. InFIGS. 19C to 19G, a dotted line represents a true value, and a solid line represents a value estimated using a plurality of Kalman filters50.

FIG. 19Cshows the estimated values found when an offset of 1 deg/sec was superimposed in the roll angular velocity sensor21after a predetermined period of time.FIG. 19Dshows the estimated values found when an offset of 1 deg/sec was superimposed in the pitch angular velocity sensor23after a predetermined period of time.FIG. 19Eshows the estimated values found when an offset of 1 deg/sec was superimposed in the yaw angular velocity sensor22after a predetermined period of time.FIG. 19Fshows the estimated values found when an offset of 100 mG was superimposed in the front-rear acceleration sensor25after a predetermined period of time.

FIG. 19Gshows the estimated values found when offsets were superimposed in all the sensors in a combined manner. Specifically, an offset of 1 deg/sec was superimposed in the roll angular velocity sensor21, an offset of 1 deg/sec was superimposed in the pitch angular velocity sensor23, an offset of 1 deg/sec was superimposed in the yaw angular velocity sensor22, an offset of 100 mG was superimposed in the top-bottom acceleration sensor24, and an offset of 100 mG was superimposed in the front-rear acceleration sensor25.

FIGS. 19C to 19Gdemonstrate that, immediately after an offset was superimposed, the estimated values of some other sensor offsets temporarily fluctuated, but converged over time. This verifies that, in each case, the values of the sensor offsets were estimated with high accuracy without being interfered with by other offsets.

The calculation unit49presupposed forFIGS. 20A to 20Gis configured such that each Kalman filter50estimates the roll angle φ, vehicle velocity Vx, roll angular velocity sensor offset bro, yaw angular velocity sensor offset bya, pitch angular velocity sensor offset bpi, and front-rear acceleration sensor offset bx, and the left-right acceleration sensor offset byis estimated taking account of the likelihood obtained based on the estimation results by the Kalman filters50.

FIGS. 20A and 20Bare graphs showing the estimated values resulting when an offset error with 100 mG was superimposed in the left-right acceleration sensor26at a predetermined moment. InFIGS. 20A and 20B, a dotted line represents a true value, a broken line represents a value estimated using one Kalman filter50, and a solid line represents a value estimated using a plurality of Kalman filters50. The data represented by the broken line is data obtained when the value of one sensor offset was additionally estimated using the characteristic equation, as discussed in the present embodiment.

InFIG. 20A, the estimated value of the left-right acceleration sensor offset byincreases after a predetermined moment, indicating that an estimation close to the true value was achieved. The estimated values of the other sensor offsets are zero, again indicating that an estimation close to the true value was achieved. ForFIG. 20A, the data represented by the broken line was not obtained through an estimation process using a likelihood, and this arrangement does not allow the left-right acceleration sensor offset byto be estimated.

FIG. 20Bdemonstrates that the solid line achieves estimation results closer to the true value than the broken line. This shows that arranging a plurality of Kalman filters50in parallel and additionally estimating an offset error based on a likelihood improves estimation accuracy.

FIGS. 20C to 20Gare graphs showing the estimated values resulting when an offset error was generated in sensors other than the left-right acceleration sensor26. InFIGS. 20C to 20G, a dotted line represents a true value, and a solid line represents a value estimated using a plurality of Kalman filters50.

FIG. 20Cshows the estimated values found when an offset of 1 deg/sec was superimposed in the roll angular velocity sensor21after a predetermined period of time.FIG. 20Dshows the estimated values found when an offset of 1 deg/sec was superimposed in the pitch angular velocity sensor23after a predetermined period of time.FIG. 20Eshows the estimated values found when an offset of 1 deg/sec was superimposed in the yaw angular velocity sensor22after a predetermined period of time.FIG. 20Fshows the estimated values found when an offset of 100 mG was superimposed in the front-rear acceleration sensor25after a predetermined period of time.

FIG. 20Gshows the estimated values found when offsets were superimposed in all the sensors in a combined manner. Specifically, an offset of 1 deg/sec was superimposed in the roll angular velocity sensor21, an offset of 1 deg/sec was superimposed in the pitch angular velocity sensor23, an offset of 1 deg/sec was superimposed in the yaw angular velocity sensor22, an offset of 100 mG was superimposed in the front-rear acceleration sensor25, and an offset of 100 mG was superimposed in the left-right acceleration sensor26.

FIGS. 20C to 20Gdemonstrate that, immediately after an offset was superimposed, the estimated values of some other sensor offsets temporarily fluctuated, but converged over time. This verifies that, in each case, the values of the sensor offsets were estimated with high accuracy without being interfered with by other offsets.

Further, the results from Examples 1 to 3 prove that an estimation with high accuracy was achieved even when the unit associated with the offset quantity estimated in the Kalman filters50varies or even when the unit associated with the offset quantity estimated by using a likelihood varies.

FIGS. 21A and 21Bcompare the estimate accuracy of the attitude estimation apparatus according to the first embodiment with the estimation accuracy of the attitude estimation apparatus according to the second embodiment.FIGS. 21A and 21Bare graphs showing the estimated values resulting when an offset was superimposed in the roll angular velocity sensor21, pitch angular velocity sensor23, yaw angular velocity sensor22, front-rear acceleration sensor25, and top-bottom acceleration sensor24.FIG. 21Bis a graph showing the difference values between the results shown inFIG. 21Aand the true values.

InFIGS. 21A and 21B, a dotted line represents a true value. A two-dot-chain line represents the results from an attitude estimation apparatus configured as a reference example. The reference example represents an attitude estimation apparatus including a roll angular velocity sensor21, a yaw angular velocity sensor22, a top-bottom acceleration sensor24, a front-rear acceleration sensor25and a left-right acceleration sensor26and including one Kalman filter50. The attitude estimation apparatus of the reference example is configured to estimate the roll angular velocity sensor offset bro, yaw angular velocity sensor offset byoand top-bottom acceleration sensor offset bz.

InFIGS. 21A and 21B, a broken line represents the results from the attitude estimation apparatus according to the first embodiment. This attitude estimation apparatus is configured to estimate the roll angular velocity sensor offset bro, yaw angular velocity sensor offset byo, pitch angular velocity sensor offset bpi, and top-bottom acceleration sensor offset bz.

InFIGS. 21A and 21B, a solid line represents the results from the attitude estimation apparatus according to the second embodiment. This attitude estimation apparatus is configured to estimate the roll angular velocity sensor offset bro, yaw angular velocity sensor offset byo, pitch angular velocity sensor offset bpi, top-bottom acceleration sensor offset bz, and front-rear acceleration sensor offset bx. In this arrangement, the roll angular velocity sensor offset bro, yaw angular velocity sensor offset byo, pitch angular velocity sensor offset bpi, and front-rear acceleration sensor offset bxare estimated by the Kalman filters50and the top-bottom acceleration sensor offset bzis estimated using a likelihood.

FromFIGS. 21A and 21B, it can be understood that the greater the number of offsets being estimated, the closer the estimated values are to true values.

Alternative Embodiments

Alternative embodiments will be described.

<1> According to the above-illustrated embodiments, information from the rear-wheel velocity sensor7provided on the rear wheel3is provided, as the rear-wheel velocity, to the calculation unit49. Alternatively, as shown inFIG. 22, information from a front-wheel velocity sensor8provided on the front wheel2may be provided, as the front-wheel velocity, to the calculation unit49. In such implementations, the above-provided equations may be calculated with the vector ρ replaced by the vector σ. For calculation, the force vector FP may be read as the force acting on the road-surface grounding point P3of the rear wheel3and the force vector FR may be read as the force acting on the road-surface grounding point P2of the front wheel2to enable the attitude estimation of the vehicle body1using the same method as described above.

<2> When the moment of the force acting on the vehicle100as viewed from the reference point O is calculated, the aerodynamic vector γ may be approximated, γ=0. In such implementations, the term with γ is not present in Equation (57); however, since the relational expression of Equation (51) is used during the process of rearranging Equation (57) to Equation (58) to replace γ with the function of another factor, the same equation as Equation (58) is found.

<3> The angular velocity sensors, i.e. roll angular velocity sensor21, yaw angular velocity sensor22and pitch angular velocity sensor23may be replaced by sensors for detecting the angular velocities about three or more axes different from the top-bottom direction, left-right direction and front-rear direction of the vehicle100. In such implementations, the detected values from the angular velocity sensors may be replaced by the roll angular velocity ωro, pitch angular velocity ωpiand yaw angular velocity ωyausing a geometrical method. Then, based on the replacement values, the attitude estimation apparatus10may perform the calculation defined by the above-provided Equations.

Similarly, in the above-illustrated embodiments, the acceleration sensors, i.e. top-bottom acceleration sensor24, front-rear acceleration sensor25and left-right acceleration sensor26may be replaced by three or more acceleration sensors for directions different from the top-bottom direction, left-right direction and front-rear direction of the vehicle100. In such implementations, the detected values from the acceleration sensors may be replaced by the top-bottom acceleration Gz, front-rear acceleration Gxand left-right acceleration Gyusing a geometrical method. Then, based on the replacement values, the attitude estimation apparatus10may perform the calculation defined by the above-provided equations.

<4> In the above-illustrated embodiments, one of the input parameters for the calculation unit49is the rear-wheel velocity vr; alternatively, the vehicle velocity detected by a velocity sensor capable of measuring the travel speed (i.e. vehicle velocity) of the vehicle100may be an input parameter for the calculation unit49. One example may be information from a GPS sensor.

In such implementations, the vehicle velocity Vx estimated by the calculation unit49may be replaced by information relating to the vehicle velocity detected by the sensor. That is, the calculation unit49may not estimate the vehicle velocity Vx.

<5> In the above-illustrated embodiments, the calculation unit49may be implemented by the ECU20and a program; alternatively, some or all of the functionalities of the calculation unit49may be implemented by hardware such as electronic circuitry.

<6> The filtering method for the Kalman filters of the present invention are not limited to the filtering for the Kalman filters50of the above-illustrated embodiments. The above example may be replaced by another adaptive filtering method. Exemplary Kalman filters may be the LMS (least mean square) adaptive filter or H∞ filtering, for example.

<9> In the above-illustrated embodiments, the attitude estimation apparatus10is applied to a motorcycle; alternatively, if similar modeling is possible, it may be applied to other vehicles such as a motor four-wheeler or motor three-wheeler or various transportation machines such as ships.

In the above-illustrated embodiments, an offset error is a value indicative of the difference between a detected value from each detection unit and an actual value, i.e. true value. Each Kalman filter receives detected values from a plurality of detection units. Further, each Kalman filter receives an imaginary offset quantity. An imaginary offset quantity is an imaginary value of an offset error for at least one of the detection units, i.e. of the first offset error. The detection unit with which the first offset error is associated is the detection unit of interest. An imaginary offset quantity is an imaginary value of the offset error for the detection unit of interest.

At least two of the imaginary offset quantities provided to the Kalman filters are different from each other. That is, at least two imaginary offset quantities that are different from each other are provided to at least two Kalman filters, respectively.

Each Kalman filter provides an estimated value of the roll angle and a likelihood. Further, each Kalman filter may provide, as an estimated value, an offset error for at least one of a plurality of detection units that is other than the detection unit of interest (i.e. second offset error). Each Kalman filter is configured to calculate the estimated value from the current estimation operation using a plurality of detected values input during the current estimation operation and an estimated value calculated during the previous estimation operation.

In the above-illustrated embodiments, each Kalman filter receives a detected value from the first angular velocity detection unit, a detected value from the second angular velocity detection unit, a detected value from the first acceleration detection unit, a detected value from the second acceleration detection unit, a detected value from the third acceleration detection unit, a detected value from the velocity information detection unit, and an imaginary offset quantity for the first offset error. The output of each Kalman filter includes at least the estimated value of the roll angle of the movable body and a likelihood.

For the current estimation operation, each Kalman filter uses at least one of the detected value from the first angular velocity detection unit, the detected value from the second angular velocity detection unit, the detected value from the first acceleration detection unit, the detected value from the second acceleration detection unit, the detection value from the third acceleration detection unit and the detected value from the velocity information detection unit, as well as the estimated value from the previous estimation operation, to calculate a current intermediate estimated value. The Kalman filter corrects the intermediate estimated value based on the result of a comparison between the previous estimated value and the observed value. The observed value may be at least one of the detected value from the first angular velocity detection unit, the detected value from the second angular velocity detection unit, the detected value from the first acceleration detection unit, the detected value from the second acceleration detection unit, the detected value from the third acceleration detection unit and the detected value from the velocity information detection unit. In the above-illustrated examples, each Kalman filter calculates a Kalman gain as an example of the value indicating the result of the comparison between the previous estimated value and observed value. The intermediate estimated value is corrected using the value indicating the result of the comparison between the previous estimated value and observed value, and the resulting value is provided as the estimated value of the current estimation operation.

In the above-illustrated embodiments, each Kalman filter calculates the likelihood based on an observation/prediction error which indicates how different the previous estimated value and observed value are. The observed value may be at least one of the detected value from the first angular velocity detection unit, the detected value from the second angular velocity detection unit, the detected value from the first acceleration detection unit, the detected value from the second acceleration detection unit, the detected value from the third acceleration detection unit and the detected value from the velocity information detection unit.

EXPLANATION OF REFERENCE CHARACTERS

1: vehicle body

2: front wheel

3: rear wheel

5: group of sensors

10: attitude estimation apparatus

12: navigation system

15: headlight driving device

16: front fork

20: electronic control unit (ECU)

21: roll angular velocity sensor

22: yaw angular velocity sensor

23: pitch angular velocity sensor

49: calculation unit

51: system equation calculation unit

52: observation equation calculation unit

56: Kalman gain calculation unit

57likelihood calculation unit

59: imaginary offset input unit

60: load estimation unit

70: suspension stroke quantity estimation unit

90: slope estimation unit

200: road surface