Method for automatically identifying speed operation range in a mechanical system driven by PMSM or induction motors under friction and load condition

As speed operation range identification system for motion systems driven by permanent magnet synchronous motors (PMSMs) or induction motors leverages both characteristics of the motor as well as dynamic characteristics of the motion system—including the friction and load—to identify suitable maximum speeds for operation of the motion system in the normal speed and field weakening regions. The identification system can model both motor characteristics as well as real-time dynamics of the controlled mechanical system that may vary during operation. The system can apply an optimization algorithm to this model to determine suitable maximum speeds for operation in the normal speed and/or field weakening regions. The determined maximum speeds can be used to perform substantially real-time adjustments to motion profile limits or current reference values generated by the motor controller in order to ensure that the speed of the system remains below the determined maximum.

TECHNICAL FIELD

This disclosure generally relates to motor control, and, more specifically, to techniques for determining speed operation ranges for mechanical systems driven by PMSM motors.

BACKGROUND

Permanent magnet synchronous motors (PMSMs) and induction motors are used in a wide variety of applications, including but not limited to motion control systems, traction or propulsion systems for electric vehicles, HVAC (heating, ventilating, and air conditioning) systems, machine tools (e.g., spindles, rotating worktables, tool articulation), pumps, and the like. In general, PMSMs can be categorized as surface-mounted PMSMs (SPMSMs) or interior-mounted PMSMs (IPMSMs) depending on how the permanent magnets are mounted relative to the rotor.

PMSMs and induction motors are typically controlled using a motor controller (e.g., using field-oriented control techniques) which controls the speed of the motor in accordance with a speed or position reference signal generated by a motion control application. The maximum speed operation range of a given motor used in a control application is typically a function of the motor characteristics as well as the motion control application characteristics, which can be complicated to assess. A designer of a motor or motion application product must consider all of these aspects when selecting a motor to use in a given motion control system, and in order determine if the selected motor can operate within the desired speed range defined by the target application.

The above-described is merely intended to provide an overview of some of the challenges facing conventional motion control systems. Other challenges with conventional systems and contrasting benefits of the various non-limiting embodiments described herein may become further apparent upon review of the following description.

SUMMARY

Systems and methods described herein leverage both characteristic parameters of a motor used to control a mechanical system as well as dynamic characteristics of the controlled mechanical system—including the friction and load—to identify suitable maximum speeds for operation of the mechanical system one or both of the normal speed and field weakening regions. In one or more embodiments, a speed operation range identification system can mathematically model both motor characteristics (e.g. stator resistance and inductance, rotor flux, number of pole pairs) as well as real-time dynamics of the controlled mechanical system that may vary during operation (e.g., frictions and load). The system can apply an optimization algorithm to this model to determine suitable maximum speeds for operation in the normal speed and/or field weakening regions. The determined maximum speeds can be used to perform substantially real-time adjustments to motion profile limits or current reference values generated by the motor controller in order to ensure that the speed of the system remains below the determined maximum.

The following description and the annexed drawings set forth herein detail certain illustrative aspects of the one or more embodiments. These aspects are indicative, however, of but a few of the various ways in which the principles of various embodiments can be employed, and the described embodiments are intended to include all such aspects and their equivalents.

DETAILED DESCRIPTION

Various embodiments are now described with reference to the drawings, wherein like reference numerals refer to like elements throughout. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide an understanding of this disclosure. It is to be understood, however, that such embodiments may be practiced without these specific details, or with other methods, components, materials, etc. In other instances, structures and devices are shown in block diagram form to facilitate describing one or more embodiments.

Permanent magnet synchronous motors (PMSMs) and induction motors are used in a wide variety of applications. For example, many industrial automation applications rely on such motors and their associated control systems to drive motion of system components (e.g., machining or material handling robots, conveyors, tooling machines, hand tools, etc.). PMSMs and induction motors are also used in the traction and/or propulsion systems of some electric vehicle designs, including but not limited to electric or hybrid electric automobiles, bicycles, forklifts and other industrial vehicles, scooters, railway vehicle such as trains, and other such vehicles. PMSMs and induction motors also have application in building infrastructure and HVAC (heating, ventilating, and air conditioning) applications that require speed or motion control, such as fans and pumps. These motors can also be found in many home and industrial appliances. For example, PMSMs or induction motors can be used drive the drums of home or industrial washing machines, to control the spinning of centrifuges, or to control the motion of other such appliances.

In general, PMSMs can be categorized as surface-mounted PMSMs (SPMSMs) or interior-mounted PMSMs (IPMSMs).FIG. 1is a simplified diagram of an example SPMSM100. SPMSM100comprises a rotor108configured to rotate within a stator102. The stator102includes a number of electrical windings104arranged to surround the rotor108. For SPMSMs, permanent magnets106are mounted on the surface of the rotor108. During operation, electrical current through the windings104sets up a magnetic field within the air gap110between the rotor108and the stator102, and the interaction between the magnets106and the magnetic field causes the rotor108to rotate, producing torque. The speed and direction of the rotor108can be controlled by controlling the current through the stator windings104. IPMSMs are similar to SPMSMs, except that the permanent magnets106are buried within the rotor108rather than being mounted on the surface.

FIG. 2is a simplified diagram of an example closed-loop control system for a motor204(either an SPMSM, an IPMSM, or an induction motor). Control system206drives the PMSM204in accordance with a speed reference signal ωRef, which is generated by a separate motion control application. A 3-phase inverter202converts direct current (DC) bus voltage VDCto controlled 3-phase AC power to the motor's stator windings, where the 3-phase AC power is controlled based on control signals (e.g., pulse width modulation signals, space vector modulation signals, etc.) generated by the control system206. For control systems that include speed or position sensors, the speed of motor204is measured and provided to the control system206, which adjusts the control signals as a function of the measured speed, the speed reference, and the measured stator currents. For sensorless control systems, which are not capable of directly measuring the motor speed, control system206estimates the angle and speed of the motor based on the measured stator currents.

Although the diagram ofFIG. 2depicts the speed of the motor being controlled by a speed reference signal, some control systems are designed to control the motor based on a position reference signal rather than or in addition to the speed reference. In such systems, the position of the motor is controlled based on a position reference signal provided to control system206.

PMSMs and induction motors are often controlled using field oriented control (FOC) techniques. According to FOC, the flux and torque components of the stator currents are controlled independently by the control system206based on the external speed reference signal ωRefand the rotor position. When operating at or below its rated or base speed, the motor can be controlled to produce a constant torque for any speed, and is therefore said to be operating in the constant torque region (also referred to as the normal speed region). In order to increase the motor speed above its base or rated speed, some FOC control systems include field-weakening control capabilities. Once the inverter202output voltage has reached its maximum voltage—typically concurrently with the motor reaching its rated speed—field weakening control can be used to weaken the air gap flux density induced by the motor's permanent magnets, allowing additional current to be sent to the motor and thus increasing the motor speed beyond the rated speed. In this mode, the PMSM is said to be operating in the field-weakening region.

For a given motion application comprising a motor and an associated control system directed by a motor control application, there is a maximum speed operation range that is a function of the motor's characteristics as well as the dynamic mechanical characteristics of the motion system (e.g., the load seen by the motor, the inertia, etc.). A designer of a motion application must consider these aspects when selecting a motor to use in the motion application, as well as to determine whether the motor can operate within the desired speed range dictated by the motion application.

During operation, typical motor applications lack the ability to evaluate, in real-time, whether a new speed reference command generated by the motion control application is within a valid speed operation range given the characteristics of the motor as well as the dynamic motion characteristics. In conventional approaches, designers may only use a model of the motor to determine valid operating ranges for a motion application. For example, using the motor model, the designer may derive a maximum torque at each speed of a range of speeds subject to the current and voltage magnitude limits. This torque-speed curve characterizes the torque generation capacity of the motor. As a result, in an ideal case, the maximum speed in the field weakening region is achieved when torque approaches zero, assuming no load and no friction. However, this model design approach does not reflect real-time system dynamics. For example, the friction and the load in a motion application will not be zero during operation, with the viscous friction in particular being proportional to the speed. For some motion applications, the characteristics of the motion system may vary by large degrees during operation. Consequently, predetermined limits on motor speed derived using a motor model may not be valid under all circumstances during operation.

To address these and other issues, systems and methods described herein relate to a speed operation range identification system for PMSM or induction motor control systems. In one or more embodiments, the speed operation range identification system executes an optimization algorithm that uses the dominant motor and motion parameters to determine, in real-time during operation, a maximum speed of a PMSM or induction motor in the normal speed range as well as in the field weakening range. In contrast to the motor model approach, the speed operation range identification system implements an approach that combines motion and motor models in order to determine the maximum speed in both the constant torque (normal speed) region and the constant power (field weakening) region. The speed operation range identification system can receive certain motor and motion parameters as inputs, some of which can be identified by any suitable online parameter estimator, thereby allowing the maximum speed values to be identified in real-time during operation of the motion system to reflect the dynamic changes in motor and motion characteristics. The maximum speed derived by the speed operation range identification system can be used to protect the motion application by adjusting motion profile limits generated by the motion control application, or for other purposes in which accurate maximum operating speeds are useful.

FIG. 3is a block diagram of an example speed operation range identification system302according to one or more embodiments. Speed operation range identification system302can include a friction input component304, a load input component306, a characteristic parameter input component308, a maximum speed determination component310, one or more processors312, and memory314. In various embodiments, one or more of the friction input component304, load input component306, characteristic parameter input component308, maximum speed determination component310, the one or more processors312, and memory314can be electrically and/or communicatively coupled to one another to perform one or more of the functions of the speed operation range identification system302. In some embodiments, components304,306,308, and310can comprise software instructions stored on memory314and executed by processor(s)312. The speed operation range identification system302may also interact with other hardware and/or software components not depicted inFIG. 3. For example, processor(s)312may interact with one or more external user interface devices, such as a keyboard, a mouse, a display monitor, a touchscreen, or other such interface devices.

Friction input component304can be configured to receive, measure, or otherwise determine Coulomb friction and viscous friction estimates; e.g., from an online parameter estimation system. In some embodiments, friction input component304may be configured to receive manually provided values of the Coulomb friction and viscous friction coefficient for a given motor system, where these values may be determined by a design engineer using independent measurement techniques. Alternatively, some embodiments of friction input component304may be configured to automatically determine values of the Coulomb friction and viscous friction coefficient based on measurements taken during operation of the motion system, or during a defined test sequence designed to output a controlled test torque command signal to the motion system, and measure corresponding velocities of the system in response to the torque command values.

Load input component306can be configured to receive, measure, or otherwise determine an amount of load on the motion system. The load value used to determine the maximum speeds may be either a constant load value seen by the motion system or a defined maximum allowable load for the motion system. Characteristic parameter input component308can be configured to receive characteristic parameters of a motor used in the motion system. These characteristic parameters can include the q-axis and d-axis stator inductances, the stator resistance, the rotor flux, and the number of pole pairs Pp of the motor. These values obtained by the friction input component304, load input component306, and characteristic parameter input component308are used by the system to model the motor and the dynamic motion characteristics of the motion system The maximum speed determination component310can be configured to determine a maximum speed for the motion system during operation based on an optimization algorithm that leverages both motor and dynamic motion models. The maximum speed determination component310can determine the maximum speed in substantially real-time based on dominant motor and motion parameters, including friction and load.

The one or more processors312can perform one or more of the functions described herein with reference to the systems and/or methods disclosed. Memory314can be a computer-readable storage medium storing computer-executable instructions and/or information for performing the functions described herein with reference to the systems and/or methods disclosed.

FIG. 4illustrates an example, non-limiting configuration for a SPMSM control system402that includes a speed operation range identification system302for determining a valid speed operating range for a mechanical system driven by a PMSM motor. AlthoughFIG. 4depicts speed operation range identification system302as an integrated component of PMSM control system402, it is to be appreciated that the identification system302is not limited to such embodiments. For example, identification system302may be embodied as a separate component that is independent of PMSM control system402, or may be used within the context of a different type of motor control system.

In an example embodiment, PMSM control system402can be implemented as part of a motor drive (e.g., a variable frequency drive) that controls motion of a PMSM424in accordance with a speed reference signal ωRefprovided by a supervisory motion control application or system. In another example embodiment, PMSM control system402may be implemented on one or more processing chips as part of an embedded system for controlling a PMSM. In yet another example embodiment, PMSM control system402can be implemented as part of a motor control module of an industrial controller for control of a PMSM used in an industrial motion control system. It is to be appreciated that the techniques described herein are not limited to these implementations.

In this example, PMSM424is a sensorless motor whose motion is controlled by PMSM control system402. However, the speed operation range identification techniques described herein are not limited to sensorless applications. In general, the PMSM control system402controls the PMSM using a flux control loop and a torque control loop. Torque reference IsqRefand the flux reference IsdRefrepresent target references for the torque and flux components, respectively, of the stator currents. To provide feedback for the flux and torque control loops, the PMSM control system402measures the stator currents on two phases of the three-phase AC power delivered to PMSM424and calculates the current for the third phase based on the values of the other two phases. Alternatively, the PMSM control system402may measure all three phases in order to obtain the stator currents. A transformation block418C transforms the stator current measurements from the three-phase A, B, C reference to the stationary α,β coordinate framework (e.g., a Clarke transformation) to yield Isα and Isβ. Transformation block418B transforms Iα and Iβ to the rotary d,q coordinate framework (e.g., a Park transformation) to yield isqand isd. Iq control component414and Id control component416compare the values of isqand isdto their corresponding reference values IsqRefand IsdRef, and adjust reference voltage values Vsqand Vsdbased on any detected errors between the measured values isqand isdand their corresponding reference values IsqRefand IsdRef.

Transformation block418A (transforms Vsqand Vsdfrom the rotary d,q framework to the stationary α,β framework (e.g., an inverse Park transform) to yield Vsqand Vsβ. Based on these values, a control signal output component, such as a space vector modulation (SVM) component or pulse width modulation (PWM) component420, controls the AC output of a 3-phase inverter422, thereby controlling motion of the PMSM. During closed-loop sensorless FOC control operation, estimation component412estimates the speed of the PMSM424based on measured stator currents Isαand Isβand reference voltage values Vsαand Vsβ. The estimated velocity ωEstis compared with a speed reference ωRef(received from a separate motion control application), and the speed control component404adjusts IsqRefas needed based on detected errors between the speed reference ωRefand the estimated velocity ωEst. Flux weakening control component410controls the value of the flux reference IsdRef. As an alternative to sensorless control, the control system402may measure the actual speed of the PMSM directly, rather than estimating the speed using estimation component412.

In accordance with one or more embodiments of this disclosure, motor control system402also includes a speed operation range identification system302capable of determining a suitable maximum speed ωMaxfor both the normal operating range as well as the field weakening range. The speed operation range identification system302can be provided with a number of motor characteristic parameters, including the stator resistance, stator inductances, rotor flux, voltage and current limits, and number of pole pairs, in order to model the motor's characteristics. Also, during execution of the motion control application, the speed operation range identification system302can receive or determine certain dynamic motion parameters—including the Coulomb friction Bc, the Viscious friction coefficient Bv, and load W (either constant load or maximum load)—and determine a maximum speed ωMaxbased on an optimization algorithm that is a function of these parameters. The optimization algorithm will be described in more detail below.

In the example illustrated inFIG. 4, the determined value of the maximum speed ωmax426is provided to the speed control component404and/or the flux weakening control component410, which can compare the maximum speed ωmaxwith reference speed ωRefand modify the references IsdRefand IsdRefas necessary (e.g., if it is determined that ωRefexceeds ωmax) to ensure that the motor speed does not exceed ωmax. However, the value of ωmaxmay also be used in other ways in various embodiments. For example, in some configurations the speed operation range identification system302may provide ωmaxto the motion control application, which may modify its generated motion profile (which sets the value of reference speed ωRef) based on the maximum speed ωmaxdetermined by the identification system302. In various embodiments, the speed operation range identification system302can be a component of a motion controller or a motor drive, or may execute on a separate system that interfaces with the motion control system (assuming the separate system is capable of measuring, calculating, or receiving the friction values required by the system do determine the maximum speeds).

FIGS. 5-7and the associated descriptions below discuss concepts and observations that form the basis for the optimization algorithm executed by the speed operation range identification system302.FIG. 5is an example plot that graphs the torque capacity of a motor as a function of speed (curve502) as determined by the motor characteristics, together with the steady state torque of the motion system as a function of speed (curve504) as determined by the combined load and friction seen by the motor. As demonstrated by curve502, the torque capacity of the motor is relatively constant from the resting state up to speed ωn. This speed range—from 0 to ωn—is referred to as the constant torque region, or normal speed region. While operating at speeds below ωn, the motor is capable of providing up to its maximum torque capacity Tmaxas needed to compensate for load and friction, as well as to provide acceleration.

If the modulation index reaches the linear boundary, field weakening can be used to achieve speeds greater than ωnif desired. While operating in this region, as the speed increases above ωn, the torque capacity of the motor begins decreasing as a function of speed, while the power P remains relatively constant at P=T*ω. Since the torque T is equal to P/ω, the torque capacity decreases as speed ω increases. This operating region above ωnis referred to as the constant power region, or field weakening region.

Curve504represents the actual torque needed to overcome friction and load in order for the motor to maintain a given speed. As demonstrated by curve504, after an initial spike in the torque to overcome initial friction when starting the motion system from rest, the steady state torque required to overcome friction and load of the motion system increases as a function of speed. In the field weakening (constant power) region, the maximum speed corresponds to ωMax(where the torque capacity curve502meets the steady state torque curve504). The torque Tfwrepresents the torque required to maintain this maximum speed ωMaxin the field weakening region.

Since the steady state torque curve504may change during operation as a function of dynamic motion system characteristics, particularly the friction and load, optimal values of the maximum speeds in the normal speed and field weakening regions may change during operation of the motion system. Consequently, designing the system assuming constant, pre-defined values for the maximum speeds may yield non-optimal performance. Accordingly, the speed operation range identification system302described herein is configured to dynamically generate a suitable maximum speed values during system operation given current system parameter dynamics.

FIG. 6is a d-q coordinate graph illustrating curves on the d-axis and q-axis current phase plane for a scenario in which the maximum speed is on the current limit circle for an IPMSM motor. The voltage limit ellipse defines the voltage limit of the inverter (e.g., inverter422). The size of this voltage limit ellipse is a function of motor speed. The current limit circle represents the current limit of the inverter (e.g., inverter422), and has a radius of Imax. Constant torque curves602and604for two respective torques are shown.

The voltage limit ellipse and the current limit circle limit the operational ranges of the d-axis and q-axis currents. The size of the voltage limit ellipse decreases as the motor speed increases. The maximum torque per ampere (MTPA) curve represents the locus of points (isd, isq) that yield a desired torque with the minimum amount of current. That is, each point on the MTPA curve represents an (isd, isq) coordinate on a given constant torque curve that is closest to the origin (thus corresponding to minimum current magnitude). As the (isd, isq) coordinates move farther from the origin, the corresponding constant torque curve represents a higher torque.

Point C inFIG. 6is the point at which the voltage limit ellipse and current limit circle intersect, and represents the point at which maximum speed is achieved without the consideration of reserved extra torque (a percentage of the rated torque) besides the maximum load and friction. The reserved extra torque is represented by the difference between constant torque curves602and604. Point A represents the maximum speed in the constant torque region (no field weakening) determined by the speed operation range identification system302described herein. Point B is the operating point representing the maximum speed in the field weakening region determined by the speed operation range identification system302described herein.

During operation in the constant torque region, the motor will speed up such that the (isd, isq) point traverses the trajectory from the origin along the MTPA curve to reach Point A (the maximum speed in the constant torque region). In the field weakening (constant power) region, coordinate (isd, isq) then continues to Point B along the constant torque curve.

FIG. 7is a d-q coordinate graph illustrating curves on the d-axis and q-axis current phase plane for a scenario in which the maximum speed is inside the current limit circle (the constant torque curve and the voltage limit ellipse share a mutual tangent line) for an IPMSM motor. In this scenario, Point C represents the maximum speed that can be achieved in the field-weakening region, without the consideration of reserved extra torque (a percentage of rated torque) besides the maximum load and friction. However, in this scenario torque values corresponding to points near Point C along the voltage limit ellipse will always be less than the torque value corresponding to Point C (i.e., such torque values would reside on different constant torque curves than that of Point C). As a result, the torque will not asymptotically converge to the speed represented by Point C. To solve this problem, a reserved torque—represented by the difference between the dashed constant torque curve702and the solid constant torque curve704—is added to the load. In this way, the identified maximum speed is actually reached at Point B, the cross point between the voltage limit ellipse and the solid constant torque curve704. Since Point B is a cross point (rather than a tangent point) of the voltage limit ellipse, the torque will asymptotically converge to this Point B given the appropriate controller setting.FIG. 7also depicts the maximum torque per flux (MTPF) curve, which represents the locus of points (isd, isq) that yield a desired torque with the minimum amount of flux, establishing the lower boundary of the field-weakening d-axis current on the DQ phase plane.

Techniques carried out by the speed operation range identification system302to determine the maximum speeds described above in real-time are now described.FIG. 8is a diagram illustrating inputs and outputs of the speed operation range identification system302for PMSM motion systems (maximum speed determination for the induction motor case will be described below in connection withFIG. 9). The system is provided with characteristic parameters of the PMSM (e.g., via input provided to the characteristic parameter input component308), including the q-axis and d-axis stator inductances Lsq and Lsd, the stator resistance RS, the rotor flux λm, and the number of pole pairs Pp of the motor. The stator resistance RSand inductances Lsq and Lsd can be measured or obtained from the motor data sheet for the PMSM. The rotor flux λmis a known or identified constant value.

The current and voltage constraints Imaxand Vmaxof the PMSM are also provided to the speed operation range identification system302. The maximum current Imaxis the maximum operational current of the PMSM and corresponds to the radius of the current limit circle (see, e.g., the current limit circles ofFIGS. 6 and 7).

In addition to the motor parameters discussed above, speed operation range identification system302is also provided with dominant motion parameters. Specifically, the system is provided with the Coulomb friction Bc and viscous friction coefficient Bv for the motion system. Friction is the resistive force resulting from the sliding contact between physical components of the motion system, such as the contact between the rotor and the shaft. The system's total friction can be modeled as a combination of its Coulomb friction Bc and viscous friction. The system's Coulomb friction Bc has a relatively constant magnitude represented by the magnitude of the friction just as the system begins moving from a state of rest. The viscous friction, which represents a frictional force which may be a function of lubrication between moving parts of the system, typically increases as a function of the speed of the motion system, and has a speed-dependent magnitude based on the viscous friction coefficient Bv. Estimates of the motion system's Coulomb friction Bc and viscous friction coefficient Bv can be determined using any suitable online parameter estimator. An example inertia and friction estimation system capable of generating such estimates is described in co-pending U.S. patent application Ser. No. 14/851,307, the entirety of which is incorporated herein by reference. The load, which can be a measured constant value or allowable maximum value, is also provided to the speed operation range identification system302.

The optimization algorithm carried out by the speed operation range identification system302given these parameters is now described. The system considers a motion model that includes Coulomb and viscous friction, as well as load W (either constant load or maximum load). At steady state, the mechanical torque Tm needed to maintain constant speed ω can be considered the sum of the viscous friction (which is a dynamic function of speed), the coulomb friction Bc, and the load W. If the viscous friction is assumed to be the viscous friction coefficient Bv multiplied by the electrical speed ω divided by the number of pole pairs Pp, which yields mechanical speed (for simplicity, speed is defined as electrical speed in this disclosure), the steady state mechanical torque needed to maintain constant speed ω can be written as:

Assume the direction of the speed is known and is reflected by the sign of Bc.

An extra torque is included in the mechanical torque equation to keep the maximum speed operating point on the d-q coordinate system away from the current limit circle and the point where the constant torque curve and the voltage limit ellipse share a mutual tangent line (as described above in connection withFIG. 7). Considering this extra torque as a percentage of the motor rated torque, this transforms equation (1) to:

Where ρ Is a percentage and Tratedis the rated torque.

As the speed increases, the torque needed to overcome viscous friction of the motion system increases. Thus, the higher the speed, the more torque that is required to maintain the speed.

In the constant torque region, an SPMSM motor keeps the d-axis current at zero in order to get the maximum torque per ampere (MTPA). When the motor speed increases such that the voltage magnitude approaches the boundary of the space vector pulse width modulation (SVPWM) linear region, in order to allow the motor to run at higher speeds, a negative d-axis current can be applied to deflux the magnetic field, and the motor thereby enters the field-weakening region.

For IPMSM motors, a difference from the SPMSM approach is that negative d-axis current is required to achieve MTPA in the normal region. An MTPA curve from torque to d-axis and q-axis currents can be obtained with respect to d-axis and q-axis inductances Lsq and Lsd and rotor flux λm. In the field-weakening region, more d-axis current is needed in order to deflux the magnetic field and reach higher speed. The maximum torque per flux (MTPF) curve establishes the lower boundary for d-axis current.

For a PMSM motor model, at steady state, the voltage and flux equations are:
Vsd=Rsisd−ωLsqisq(3)
Vsq=Rsisq+ω(Lsdisd+λm)  (4)

The voltage and current limits of the system are:
Vsd2+Vsq2≤Vmax2(5)
isd2+isq2≤Imax2(6)

The electrical torque Te is given by:
Te=1.5Pp(λm+(Lsd−Lsq)isd)isq(7)

The technique implemented by the speed operation range identification system302to determine the maximum speed for operation in the constant torque region is now described. In general, the MTPA is applied in the constant torque region in order to minimize the current. Therefore, a predefined MTPA curve establishes the unique mappings between the desired torque and the d-axis and q-axis current. These MPTA-based mappings may be defined and stored on the system302(e.g., in memory314) in the form of a look-up table, or may be determined by the system based on an approximated polynomial expression obtained from measurement or via mathematical derivation and stored in memory314, as represented by generalized equations (8) and (9) below:
isdƒsd,mtpa(Te)  (8)
isq=ƒsq,mtpa(Te)  (9)

Functions (8) and (9) yield, for a given torque Te, the point (isd, isq) corresponding to the intersection of the constant torque curve for Te and the MTPA curve for the modeled motion system.

If it is assumed that
T=Tm=Te(10)

that is, the electrical torque Te is equal to the mechanical torque Tm, then equation (2) can be substituted into equations (8) and (9) to obtain:

In the particular case of a surface-mounted PMSM motor (SPMSM), for which the stator q-axis and d-axis inductances are equal (Lsd=Lsq), the stator d-axis and q-axis currents are given as:

For maximum speed in the constant torque region, the optimization problem is defined as:
min(−ω)  (15)

In general, equations (3), (4), (5), and (6) are criteria that ensure the maximum voltage and current of the system are not exceeded, and equations (11) and (12) are criteria that place the d-axis and q-axis currents on the MTPA curve (note that, in the case of SPMSM motors, equations (11) and (12) can be replaced with equations (13) and (14)). The maximum speed determination component310of the speed operation range identification system302can thus determine the maximum speed for operation in the constant torque region by applying any suitable optimization method to solve the optimization problem (15) subject to equations (3), (4), (11), (12), (5), and (6). For example, in some embodiments the maximum speed determination component310may apply a Newton-Raphson optimization method to solve the optimization problem and thereby determine the maximum speed. As discussed above, the speed operation range identification system302can then provide this maximum speed value to another component of the control system (e.g., the speed control component404or the flux weakening component410) to ensure that the control outputs to the motor do not produce speeds in excess of this maximum speed. In other embodiments, the system302may send the determined maximum speed to a separate motion control application, which can then adjust the limits of the motion profile generated by the motion control application to comply with this determined speed limit.

The technique implemented by the speed operation range identification system302to determine the maximum speed for operation in the constant power (field weakening) region is now described. As noted above, the motion system enters the constant power or field weakening region by applying a negative d-axis current in order to deflux the magnetic field and achieve higher speeds. In the present approach for determining the maximum speed for the constant power region, the MTPF curve is used to limit the lower bound of the d-axis current. To this end, the system302can store information describing a unique mapping between the d-axis and q-axis currents along the MTPF curve. This mapping information may be stored as a look-up table or other storage format in memory314, or as an approximated polynomial expression obtained from measurement or mathematical derivation, as represented by:
isd=ƒsd,mtpf(Isq)  (16)

For the maximum speed in the constant power (field weakening) region, the optimization problem is defined as:
min(−ω)  (17)

Equations (3), (4), (5), and (6) ensure the maximum voltage and current of the system are not exceeded. Equation (20) represents negative d-axis current in accordance with operation in the field weakening region. Equation (19) places a lower bound on the d-axis current corresponding to the MTPF curve (note that the MTPA curve is an upper bound in the field weakening region, but the optimal solution will always yield a value of d-axis current that is less than the MTPA curve, so this constrain is accounted for). As in the constant torque region, the maximum speed determination component310of the speed operation range identification system302can determine the maximum speed in the constant power region by applying any suitable optimization method to solve the optimization problem (17) subject to equations (3), (4), (18), (19), (20), (5), and (6). For example, in some embodiments the system may apply a Newton-Raphson optimization method to solve the optimization problem and thereby determine the maximum speed. As in the constant torque example described above, the speed operation range identification system302can then provide this maximum speed value to another component of the control system (e.g., the speed control component404, the flux weakening component410) or to a separate motion control application, which adjust their outputs in compliance with this maximum speed limit.

The examples above describe techniques for determining the maximum speed for motions systems that utilize a PMSM motor. Similar techniques can be used to determine a maximum operating speed for motion systems that include induction motors as well. Determination of a speed operation range for an induction motor is similar to the technique used for PMSMs, with modifications to allow for the fact that the magnetic field of an induction motor is generated from stator magnetizing current, rather than a permanent magnet as in the PMSM case.

FIG. 9is a diagram illustrating inputs and outputs of another embodiment of the speed operation range identification system302for motions systems using an induction motor (rather than a PMSM motor). Similar to the PMSM case, the system is provided with characteristic parameters of the induction (e.g., via input provided to the characteristic parameter input component308), including the stator inductances Ls, the stator resistance RS, the rotor inductance Lr, the rotor resistance Rr, the mutual inductance Lm, and the number of pole pairs Pp of the motor.

The current and voltage constraints Imaxand Vmaxare also provided to the speed operation range identification system302. In the induction motor case, the maximum magnetizing current Id,maxof the induction motor is also provided to the system.

Also similar to the PMSM scenario, speed operation range identification system302is also provided with dominant motion parameters. Specifically, the system is provided with the Coulomb friction Bc and viscous friction coefficient Bv for the motion system. Estimates of the motion system's Coulomb friction Bc and viscous friction coefficient Bv can be determined using any suitable online parameter estimator. The load, which can be a measured constant value or allowable maximum value, is also provided to the speed operation range identification system302.

The optimization algorithm carried out by the speed operation range identification system302for the induction motor case given these parameters is now described. At steady state, the voltage and flux equations of an induction motor model can be given as:

Where σ is a leakage factor of the induction motor, and is given by:

As in the PMSM case, the voltage and current limits of the induction motor system can be given as:
Vsd2+Vsq2≤Vmax2(5)
isd2+isq2≤Imax2(6)

The electrical torque T can be given as:

The technique implemented by the speed operation range identification system302to determine the maximum speed for operation in the constant torque region for the induction motor scenario is now described. The MTPA is applied in the constant torque region to minimize the current. The flux of the induction motor is controlled through magnetizing current. The MTPA solution in the constant torque region is
isd=isq(26)

For the maximum speed in the constant torque region, the optimization problem can be defined as:
min(−ω)  (28)

Equations (21), (22), (5), (6), and (30) ensure that the maximum voltage and current of the system are not exceeded. Equation (26) enforces the MTPA solution in the constant torque region. As in previous examples, the maximum speed determination component310of the speed operation range identification system302can determine the maximum speed in the constant torque region by applying any suitable optimization method to solve the optimization problem (26) subject to equations (21), (22), (27), (26), (29), (5), (6), and (30). For example, in some embodiments the system may apply a Newton-Raphson optimization method to solve the optimization problem and thereby determine the maximum speed. As in the PMSM examples described above, the speed operation range identification system302can then provide this maximum speed value for the induction motor system to another component of the control system or to a separate motion control application, which adjust their outputs in compliance with this maximum speed limit.

The technique implemented by some embodiments of the speed operation range identification system302to determine the maximum speed for operation in the constant power (field weakening) region for motion systems including induction motors is now described. In the field weakening region, isdis reduced to allow the increase of speed with the tradeoff of torque. Thus, unlike the constant torque region solution, isdis not equal to isq, and equation (23) replaces equation (27) in the solution. For the maximum speed in the constant power (field weakening) region, the optimization problem is defined as:
min(−ω)  (31)

As in previous examples, a Newton-Raphson optimization, or any other suitable optimization method can be used to solve the optimization problem (30) subject to equations (21), (22), (23), (28), (5), (6), and (30).

FIGS. 10-11illustrate example methodologies in accordance with certain disclosed aspects. While, for purposes of simplicity of explanation, the methodologies are shown and described as a series of acts, it is to be understood and appreciated that the disclosed aspects are not limited by the order of acts, as some acts may occur in different orders and/or concurrently with other acts from that shown and described herein. For example, those skilled in the art will understand and appreciate that a methodology can alternatively be represented as a series of interrelated states or events, such as in a state diagram. Moreover, not all illustrated acts may be required to implement a methodology in accordance with certain disclosed aspects. Additionally, it is to be further appreciated that the methodologies disclosed hereinafter and throughout this disclosure are capable of being stored on an article of manufacture to facilitate transporting and transferring such methodologies to computers.

FIG. 10is a flowchart of an example methodology1000for dynamically determining a maximum speed value for operation of a motion system in the constant torque (normal speed) region. Initially, at1002, MTPA data for a motion system is stored for reference, where the MTPA data maps a range of torque values to corresponding d-axis and q-axis current values on the MTPA curve for the motion system. In some embodiments, this MTPA data may be stored as a look-up table that defines correspondences between desired torque values and points (isd, isq) on the MTPA curve; that is, the d-axis and q-axis values that yield the maximum torque per ampere for the given desired torque, based on the intersection of the constant torque curve for the desired torque and the MTPA curve for the motion system. In other embodiments, the MTPA data may be stored as an approximated polynomial expression obtained from measurement or mathematical derivation (e.g., as represented by equations (8) and (9) above), such that providing a value of the desired torque to the expression will yield the corresponding point (isd, isq) on the MTPA curve.

At1004, the coulomb friction, viscous friction coefficient, and load of the motion system are determined. One or more of these values can be determined, for example, using an online motor/motion parameter estimator that determines these friction and load values based on measurements taking on the motion system during operation. Alternatively, one or more of the values can be determined separately and provided by a system designer. At1006, the steady state torque of the motion system is modeled as a function of speed. In particular, the steady state torque is modeled as the sum of the motion system's coulomb friction, viscous friction (which is a variable function of speed; e.g., the product of speed and the viscous friction coefficient divided by the number of pole pairs), load, and a percentage of the rated torque representing a reserved extra toque. Since the viscous friction is a dynamic function of the speed of the motion system (e.g., the product of the speed and the viscous friction coefficient determined at step1004divided by the number of pole pairs), the resulting torque model will be a function of the speed of the motion system (e.g., as represented by equation (2) above).

At1008, an optimization algorithm is executed that determines, based on the steady state torque model yielded at step1006and the MTPA data stored at step1002, a maximum speed value for operation of the motion system in the constant torque region that yields a steady state torque having d-axis and q-axis currents that are on the MTPA curve and that do not cause the motion system to exceed the current and voltage constraints of the motion system (as determined based on characteristic parameters of the motor, including the q-axis and d-axis stator inductances, the stator resistance, and the rotor flux). In an example technique, this maximum speed can be determined by finding the maximum speed subject to equations (3), (4), (11), (12), (5), and (6) above.

At1010, the maximum speed determined at step1008is output to a motion control system or a motion control application for regulation of motion profile limits. For example, the determined maximum speed value may be used by a motion control application that generates motion profiles for control of the motor to limit the maximum speed set by the motion profiles, where the maximum speed is used to limit the speed while operating in the constant torque region. In another example, the maximum speed value may be provided to the speed controller and/or flux weakening controllers of a motor control system that translates the speed reference signal defined by the motion profiles into reference currents that control the motor control output signal. In such examples, the controllers may use the determined maximum speed to regulate the q-axis and/or d-axis current to ensure that the control output signal does not exceed the maximum speed while operating in the constant torque region.

FIG. 11is a flowchart of an example methodology1100for dynamically determining a maximum speed value for operation of a motion system in the constant power (field weakening) region. Initially, at1102, MTPF data is stored that maps a range of d-axis current values to corresponding q-axis current values on the MTPF curve for the motion system. This MTPF data may be stored in the form of a look-up table that defines correspondences between q-axis current values and respective d-axis current values that place the point (isd, isq) on the MTPF curve for the motion system. Alternatively, the MTPF data may be stored as an approximated polynomial expression (as represented by equation (16) above).

At1104, the coulomb friction, the viscous friction coefficient, and the load of the motion system is determined, in a manner similar to step1004of methodology1000above. At1106, the steady state torque is modeled as a function of speed for the motion system, in a manner similar to step1006of methodology1000. At1108, an optimization algorithm is executed that determines, based on the steady state torque model and the MTPF data, a maximum speed value for operation of the motion system in the constant power region that does not cause the motion system to exceed the voltage and current constraints (as determined by the characteristic parameters of the motor, including the q-axis and d-axis stator inductances, the stator resistance, and the rotor flux), and that yields a d-axis current that is greater than or equal to the MTPF curve. That is, the optimization algorithm uses the MTPF curve of the motion system as a lower bound on the d-axis current when determining the maximum speed for operation in the field weakening region. At1110, the maximum speed value determined at step1108is output to a motion control system or a motion control application for regulation of motion profile limits, in a similar manner to step1010of methodology1000.

Exemplary Networked and Distributed Environments

One of ordinary skill in the art can appreciate that the various embodiments described herein can be implemented in connection with any computer or other client or server device, which can be deployed as part of a computer network or in a distributed computing environment, and can be connected to any kind of data store where media may be found. In this regard, the various embodiments of the speed operation range identification system described herein can be implemented in any computer system or environment having any number of memory or storage units (e.g., memory314ofFIG. 3), and any number of applications and processes occurring across any number of storage units. This includes, but is not limited to, an environment with server computers and client computers deployed in a network environment or a distributed computing environment, having remote or local storage. For example, with reference toFIG. 3, friction input component304, load input component306, characteristic parameter input component308, and maximum speed determination component310can be stored on a single memory314associated with a single device, or can be distributed among multiple memories associated with respective multiple devices. Similarly, friction input component304, load input component306, characteristic parameter input component308, and maximum speed determination component310can be executed by a single processor312, or by multiple distributed processors associated with multiple devices.

Distributed computing provides sharing of computer resources and services by communicative exchange among computing devices and systems. These resources and services include the exchange of information, cache storage and disk storage for objects. These resources and services can also include the sharing of processing power across multiple processing units for load balancing, expansion of resources, specialization of processing, and the like. Distributed computing takes advantage of network connectivity, allowing clients to leverage their collective power to benefit the entire enterprise. In this regard, a variety of devices may have applications, objects or resources that may participate in the various embodiments of this disclosure.

FIG. 12provides a schematic diagram of an exemplary networked or distributed computing environment. The distributed computing environment includes computing objects1210,1212, etc. and computing objects or devices1220,1222,1224,1226,1228, etc., which may include programs, methods, data stores, programmable logic, etc., as represented by applications1230,1232,1240,1236,1238. It can be appreciated that computing objects1210,1212, etc. and computing objects or devices1220,1222,1224,1226,1228, etc. may comprise different devices, such as personal digital assistants (PDAs), audio/video devices, mobile phones, MP3 players, personal computers, laptops, tablets, etc., where embodiments of the speed operation range identification system described herein may reside on or interact with such devices.

Each computing object1210,1212, etc. and computing objects or devices1220,1222,1224,1226,1228, etc. can communicate with one or more other computing objects1210,1212, etc. and computing objects or devices1220,1222,1224,1226,1228, etc. by way of the communications network1240, either directly or indirectly. Even though illustrated as a single element inFIG. 12, communications network1240may comprise other computing objects and computing devices that provide services to the system ofFIG. 12, and/or may represent multiple interconnected networks, which are not shown. Each computing object1210,1212, etc. or computing objects or devices1220,1222,1224,1226,1228, etc. can also contain an application, such as applications1230,1232,1240,1236,1238, that might make use of an API, or other object, software, firmware and/or hardware, suitable for communication with or implementation of various embodiments of this disclosure.

Thus, a host of network topologies and network infrastructures, such as client/server, peer-to-peer, or hybrid architectures, can be utilized. The “client” is a member of a class or group that uses the services of another class or group. A client can be a computer process, e.g., roughly a set of instructions or tasks, that requests a service provided by another program or process. A client process may utilize the requested service without having to “know” all working details about the other program or the service itself.

In a client/server architecture, particularly a networked system, a client can be a computer that accesses shared network resources provided by another computer, e.g., a server. In the illustration ofFIG. 12, as a non-limiting example, computing objects or devices1220,1222,1224,1226,1228, etc. can be thought of as clients and computing objects1210,1212, etc. can be thought of as servers where computing objects1210,1212, etc. provide data services, such as receiving data from client computing objects or devices1220,1222,1224,1226,1228, etc., storing of data, processing of data, transmitting data to client computing objects or devices1220,1222,1224,1226,1228, etc., although any computer can be considered a client, a server, or both, depending on the circumstances. Any of these computing devices may be processing data, or requesting transaction services or tasks that may implicate the techniques for systems as described herein for one or more embodiments.

In a network environment in which the communications network/bus1240is the Internet, for example, the computing objects1210,1212, etc. can be Web servers, file servers, media servers, etc. with which the client computing objects or devices1220,1222,1224,1226,1228, etc. communicate via any of a number of known protocols, such as the hypertext transfer protocol (HTTP). Computing objects1210,1212, etc. may also serve as client computing objects or devices1220,1222,1224,1226,1228, etc., as may be characteristic of a distributed computing environment.

Exemplary Computing Device

As mentioned, advantageously, the techniques described herein can be applied to any suitable device. It is to be understood, therefore, that handheld, portable and other computing devices and computing objects of all kinds are contemplated for use in connection with the various embodiments. Accordingly, the below computer described below inFIG. 13is but one example of a computing device. Additionally, a suitable server can include one or more aspects of the below computer, such as a media server or other media management server components.

FIG. 13thus illustrates an example of a suitable computing system environment1300in which one or aspects of the embodiments described herein can be implemented, although as made clear above, the computing system environment1300is only one example of a suitable computing environment and is not intended to suggest any limitation as to scope of use or functionality. Neither is the computing system environment1300be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary computing system environment1300.

With reference toFIG. 13an exemplary computing device for implementing one or more embodiments in the form of a computer1310is depicted. Components of computer1310may include, but are not limited to, a processing unit1320, a system memory1330, and a system bus1322that couples various system components including the system memory to the processing unit1320. Processing unit1320may, for example, perform functions associated with processor(s)312of speed operation range identification system302, while system memory1330may perform functions associated with memory314.

Computer1310typically includes a variety of computer readable media and can be any available media that can be accessed by computer1310. The system memory1330may include computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM) and/or random access memory (RAM). By way of example, and not limitation, system memory1330may also include an operating system, application programs, other program modules, and program data.

A user can enter commands and information into the computer1310through input devices1340, non-limiting examples of which can include a keyboard, keypad, a pointing device, a mouse, stylus, touchpad, touchscreen, trackball, motion detector, camera, microphone, joystick, game pad, scanner, or any other device that allows the user to interact with computer1310. A monitor or other type of display device is also connected to the system bus1322via an interface, such as output interface1350. In addition to a monitor, computers can also include other peripheral output devices such as speakers and a printer, which may be connected through output interface1350. In one or more embodiments, input devices1340can provide user input to speed operation range identification system302, while output interface1350can receive and display information relating to operations of speed operation range identification system302.

The computer1310may operate in a networked or distributed environment using logical connections to one or more other remote computers, such as remote computer1370. The remote computer1370may be a personal computer, a server, a router, a network PC, a peer device or other common network node, or any other remote media consumption or transmission device, and may include any or all of the elements described above relative to the computer1310. The logical connections depicted inFIG. 13include a network1372, such local area network (LAN) or a wide area network (WAN), but may also include other networks/buses e.g., cellular networks.

As mentioned above, while exemplary embodiments have been described in connection with various computing devices and network architectures, the underlying concepts may be applied to any network system and any computing device or system in which it is desirable to publish or consume media in a flexible way.

Also, there are multiple ways to implement the same or similar functionality, e.g., an appropriate API, tool kit, driver code, operating system, control, standalone or downloadable software object, etc. which enables applications and services to take advantage of the techniques described herein. Thus, embodiments herein are contemplated from the standpoint of an API (or other software object), as well as from a software or hardware object that implements one or more aspects described herein. Thus, various embodiments described herein can have aspects that are wholly in hardware, partly in hardware and partly in software, as well as in software.

As mentioned, the various techniques described herein may be implemented in connection with hardware or software or, where appropriate, with a combination of both. As used herein, the terms “component,” “system” and the like are likewise intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on computer and the computer can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers. Further, a “device” can come in the form of specially designed hardware; generalized hardware made specialized by the execution of software thereon that enables the hardware to perform specific function (e.g., coding and/or decoding); software stored on a computer readable medium; or a combination thereof.

In order to provide for or aid in inferences described herein, components described herein can examine the entirety or a subset of the data to which it is granted access and can provide for reasoning about or infer states of the system, environment, etc. from a set of observations as captured via events and/or data. Inference can be employed to identify a specific context or action, or can generate a probability distribution over states, for example. The inference can be probabilistic—that is, the computation of a probability distribution over states of interest based on a consideration of data and events. Inference can also refer to techniques employed for composing higher-level events from a set of events and/or data.

Such inference can result in the construction of new events or actions from a set of observed events and/or stored event data, whether or not the events are correlated in close temporal proximity, and whether the events and data come from one or several event and data sources. Various classification (explicitly and/or implicitly trained) schemes and/or systems (e.g., support vector machines, neural networks, expert systems, Bayesian belief networks, fuzzy logic, data fusion engines, etc.) can be employed in connection with performing automatic and/or inferred action in connection with the claimed subject matter.