Vibration type actuator control apparatus, vibration type driving apparatus having the same, interchangeable lens, imaging apparatus, and automatic stage

A vibration type actuator control apparatus, which uses a vibration from a vibrator to move a contact member, includes a control unit and a drive unit. The control unit includes first and second learned models, each having a neural network, and outputs control amounts for the drive unit to move the contact member. When a contact member moving target velocity is input, the first learned model outputs a first control amount as one of the control amounts. When a positional deviation is input, the second learned model outputs a second control amount as one of the control amounts. The drive unit moves the contact member using a value based on the first and second control amounts. The positional deviation is in association with a difference between a target position for moving the contact member and a detected position detected when the contact member is moved relative to the vibrator.

BACKGROUND

Field

The present disclosure relates to a vibration type actuator control apparatus, a vibration type driving apparatus having the same, an interchangeable lens, an imaging apparatus, and an automatic stage.

Description of the Related Art

A vibration type motor as an example of a vibration type actuator will now be described. A vibration type motor is a non-electromagnetic drive motor. More specifically, the electro-mechanical energy converter generates a high-frequency vibration when an electro-mechanical energy converter, such as a piezoelectric element bonded to an elastic member, is applied with an alternating-current (AC) voltage, and takes out vibration energy of the electro-mechanical energy converter as a continuous mechanical movement.

A vibration type motor has excellent motor features, such as compact size, light weight, high accuracy, and high torque at low-speed drive in comparison with an electromagnetic drive motor. However, a vibration type motor has non-linear motor characteristics and therefore is hard to be modeled. Moreover, since controllability of a vibration type motor changes according to drive conditions or temperature environment, a control system needs to be devised. A vibration type motor has many control parameters including the frequency, phase difference, and voltage, and the adjustment of these control parameters is complicated.

FIG.20Ais a control block diagram illustrating a conventional common vibration type driving apparatus based on proportional-integral-derivative (PID) control (refer to Japanese Patent Application Laid-Open No. 2016-144262). A 2-phase AC voltage (AC signal) is output from a drive circuit to which a control amount is input described below. The actual velocity (detected velocity) of a vibration type motor (vibration type actuator) can be controlled by controlling the frequency (1/period), phase difference, and voltage amplitude (refer toFIG.20B) of the 2-phase AC voltage that are output from the drive circuit. The voltage amplitude is variable by using the pulse width signal input to the drive circuit from a PID controller (described below).FIG.20B(1), (2), and (3) illustrate the period, phase difference, and voltage amplitude of the 2-phase AC voltage, respectively, output from the drive circuit.

A positional deviation is input into the PID controller (control amount output unit): the positional deviation is a difference between the target position of the vibration type motor specified by a position instruction unit and the actual position (detected position) of the vibration type motor detected by a position detection unit. Then, control amounts successively output from the PID controller at control sampling intervals are input into the drive circuit. The control amounts (frequency, phase difference, and pulse width) have been subjected to the PID operation according to the positional deviation input to the PID controller.

Position feedback control is then performed based on the control amounts. Hereinafter, the control sampling interval is also simply referred to as a “sampling interval”.

FIG.20Cschematically illustrates the frequency vs. velocity characteristics of the vibration type actuator. More specifically,FIG.20Cillustrates a high velocity and a large gradient of the frequency vs. velocity characteristics at a frequency (f1) in the high-velocity region (low-frequency range), and a low velocity and a small gradient of the frequency vs. velocity characteristics at a frequency (f2) in the low-velocity region (high-frequency range). A vibration type actuator provides different control performance (frequency vs. velocity characteristics and phase difference vs. velocity characteristics) for each velocity region to be used, making it hard to adjust the PID control gain.FIG.20Dschematically illustrates the phase difference vs. velocity characteristics of the vibration type actuator. More specifically,FIG.20Dillustrates a high velocity and a large gradient of the phase difference vs. velocity characteristics at the frequency (f1) in the high-velocity region (low-frequency range), and a low velocity and a small gradient of the phase difference vs. velocity characteristics at the frequency (f2) in the low-velocity region (high-frequency range).

A vibration type actuator provides different control performances (frequency vs. velocity characteristics and phase difference vs. velocity characteristics) for each velocity region to be used, making it hard to adjust the PID control gain.FIG.20Dschematically illustrates the phase difference vs. velocity characteristics of the vibration type actuator. More specifically,FIG.20Dillustrates a high velocity and a large gradient of the phase difference vs. velocity characteristics at the frequency (f1) in the high-velocity region (low-frequency range), and a low velocity and a small gradient of the phase difference vs. velocity characteristics at the frequency (f2) in the low-velocity region (high-frequency range).

When the ambient temperature changes, for example, when the temperature changes from a normal temperature to a low temperature, the resonance frequency shifts from the low-frequency side to the high-frequency side based on the temperature characteristics of the piezoelectric element. In this case, the control performance also changes by the ambient temperature because the velocity in association with the same drive frequency, and the gradient of the frequency vs. velocity characteristics in association with the drive frequency are different before and after the resonance frequency shifts from the low-frequency side to the high-frequency side.

Since the velocity and the gradient are different for each individual vibration type motor, the control performance also changes on an individual basis.

Further, the control performance also changes by change over time. In designing, it is necessary to adjust the PID control gain (proportional gain, integral gain, and differential gain in PID control) in consideration of all of these change factors to ensure a gain margin and a phase margin. There has been a demand for a vibration type actuator control apparatus having a control amount output unit different from the conventional PID controller, as a main control amount output unit.

SUMMARY

The present disclosure is directed to providing a vibration type actuator control apparatus having a control amount output unit different from a conventional proportional-integral-derivative (PID) controller, as a main control amount output unit.

According to an aspect of the present disclosure, a vibration type actuator control apparatus configured to move, by using a vibration produced in a vibrator, a contact member in contact with the vibrator relative to the vibrator includes a control unit including a first learned model and a second learned model each having a neural network including an input layer, a hidden layer, and an output layer, and a drive unit configured to move the contact member in contact with the vibrator relative to the vibrator by using control amounts output from the control unit, wherein, in a case where a target velocity for moving the contact member relative to the vibrator is input, the first learned model is configured to output a first control amount for moving the contact member relative to the vibrator, wherein, in a case where a positional deviation for moving the contact member relative to the vibrator is input, the second learned model is configured to output a second control amount for moving the contact member relative to the vibrator, wherein the drive unit is configured to move the contact member relative to the vibrator by using a value based on the first control amount and the second control amount, and wherein the positional deviation is a value in association with a difference between a target position for moving the contact member relatively to the vibrator and a detected position detected when the contact member is moved relatively to the vibrator.

DESCRIPTION OF THE EMBODIMENTS

FIG.1is a control block diagram illustrating a vibration type actuator according to a first exemplary embodiment. A vibration type driving apparatus17includes a control apparatus15and a vibration type motor13(vibration type actuator). Referring toFIG.1, excluding the vibration type actuator13from the vibration type driving apparatus17forms the control apparatus15.

The control apparatus15includes a control unit10that controls the vibration type actuator13, a machine learning unit12that generates a learned model, and a drive unit11. The control apparatus15includes a position detection unit14that detects the position (relative position) of a contact member132relative to a vibrator131, and a velocity detection unit16that detects the velocity (relative velocity) of the contact member132relative to the vibrator131. In the following descriptions, the relative position detected by the position detection unit14is also referred to as a detected position. In the following descriptions, the relative velocity detected by the velocity detection unit16is also referred to as a detected velocity. Although an absolute encoder or an increment encoder is used as the position detection unit14, the position detection unit14is not limited thereto. The velocity detection unit16is not limited to a device (velocity sensor) that directly detects velocity information. The velocity detection unit16may be a device that indirectly detects velocity information, for example, a device that detects velocity information by calculating positional information.

The control unit10is configured to add control amounts output by two different learned models to generate a signal for controlling the drive of the vibrator131(the movement of the contact member132relative to the vibrator131). More specifically, the target velocity and the positional deviation of the vibration type actuator13are separately input to the learned models, and the output phase difference and frequency are used as control amounts. The target velocity refers to a velocity set to be followed by the actual velocity (detected velocity) when the contact member132is moved relatively to the vibrator131. The positional deviation refers to the difference between the target position and the actual position (detected position). The target position refers to a position set to be followed by the actual position (detected position) when the contact member132is moved relative to the vibrator131. A pulse width for changing the voltage amplitude may be used as a control amount.

The control unit10is configured to generate a signal for controlling the drive of the vibrator131(a relative movement of the contact member132with respect to the vibrator131). More specifically, the target velocity and the positional deviation are input to the learned models, and the output phase difference and frequency are used as control amounts of the vibration type actuator13. The target velocity refers to a velocity set to be followed by the actual velocity (detected velocity) when the contact member132is moved relatively to the vibrator131. The positional deviation refers to the difference between the target position and the actual position (detected position). The target position refers to a position set to be followed by the actual position (detected position) when the contact member132is moved relatively to the vibrator131. A pulse width for changing the voltage amplitude may be used as a control amount.

The control unit10includes a velocity generation unit101that generates a target velocity, and a position generation unit102that generates a target position. The control unit10also includes a control amount output unit including a first learned model103to which the target velocity is input and from which the phase difference and frequency are output. The control unit10also includes a control amount output unit including a second learned model107to which the positional deviation is input and from which the phase difference and frequency are output. In the following descriptions, a “control amount output unit including a learned model” is also simply referred to as a “learned model”. The control unit10also includes an adder108that adds the output from the first learned model103and the output from the second learned model107.

The drive unit11includes an AC signal generation unit104and a booster circuit105.

The velocity generation unit101generates a target velocity of a relative velocity between the vibrator131and the contact member132for each unit time. The target position of the relative position for each unit time is generated by the position generation unit102. The value in association with the difference between the detected position detected by the position detection unit14and the target position is calculated as a positional deviation (shift amount).

For example, the target velocity is output from the velocity generation unit101at control sampling intervals as a unit time. For example, the target position is output from the position generation unit102at control sampling intervals as a unit time. More specifically, one command value representing the target velocity is output from the velocity generation unit101at control sampling intervals, and one command value representing the target position is output from the position generation unit102at control sampling intervals. The command value may be a value associated with the target velocity instead of the target velocity itself. The “velocity generation unit101” outputs a command value (issues a target velocity instruction) and therefore may also be referred to as a “velocity instruction unit101”. The “position generation unit102” outputs a command value (issues a target position instruction) and therefore may also be referred to as a “position instruction unit102”.

The control sampling interval refers to one cycle ranging from the acquisition of the positional deviation to the timing immediately before the next acquisition of the positional deviation, as illustrated inFIG.1. The one cycle also includes an output of the control amount, an application of an AC voltage to the vibrator131, and a detection of the actual velocity (detected velocity) and the actual position (detected position) between these acquisitions. In the above described cycle, the position or velocity of the vibration type actuator is subjected to feedback control.

The target velocity is given to make the vibration type actuator follow a predetermined position, and may be generated by differentiating the target position for each unit time. On the contrary, the target position may be generated by integrating the target velocity.

The target velocity is input to the first learned model103which then outputs a first control amount. Meanwhile, the positional deviation is input to the second learned model107which then outputs a second control amount. The first and the second control amounts are added by the adder108which then outputs the addition value to the drive unit11as control amounts (phase difference and frequency).

According to the present exemplary embodiment, the control amounts according to the target velocity are output from the first learned model103. For the control deviation arising from drive conditions and temperature environment, a control amount that supplements an error is output from the second learned model107. This enables implementing high-accuracy and high-robustness control.

The control amounts (phase difference and frequency) output from the control unit10are input to the AC signal generation unit104to be used to control the velocity and the drive direction of the vibration type actuator. The AC signal generation unit104generates a 2-phase AC signal based on the phase difference, frequency, and pulse width. The booster circuit105including, for example, a coil and a transformer boosts the AC signal to a desired drive voltage. The boosted AC signal is applied to the piezoelectric element of the vibrator131to drive the contact member132.

The machine learning unit12performs machine learning for generating the first learned model103and the second learned model107. The detected velocity detected by the velocity detection unit16and the control amounts (phase difference and frequency) output from the control unit10are input to a first learning unit110, and are used as learning data (teacher data) for generating the first learned model103. The positional deviation and the differential values of the control amounts (phase difference and frequency) from the control unit10are input to a second learning unit111, and are used as learning data for generating the second learned model107.

The reason why the second learning unit111inputs the differential values of the control amounts from the differentiator112is to convert the values into data correlated with the positional deviation. More specifically, the differential values represent time variations of the control amounts and therefore have an association with the behavior of the positional deviation.

The second learning unit111may use other learning data. For example, the parameters (weights and threshold values) of a neural network (hereinafter also referred to as “NN”) learned by the first learning unit110can be used after being adjusted at a predetermined proportion.

FIG.2illustrates a configuration of the machine learning unit12according to the present exemplary embodiment. The first learning unit110and the second learning unit111perform NN-based machine learning. The first learning unit110will now be described. The detection velocity is input to a first learning model (NN)901which then outputs z as a result of operation. An error between a control amount and the output z is calculated, and then machine learning902is performed. By repeating the above-described machine learning902, the weights and threshold values of the first learning model901are updated to be optimized. This also applies to the second learning unit111. The positional deviation is input to the second learning model (NN)903which then outputs z. An error between the differential value of a control amount and the output z is calculated, and then machine learning904is performed. The machine learning will be described in detail below.

FIGS.3A to3Cillustrate a neural network structure (hereinafter also referred to as a “NN structure”) included in each learning model according to the present exemplary embodiment.

The first learned model103and the second learned model107include an NN composed of a layer X as an input layer, a layer H as a hidden layer, and a layer Z as an output layer. The first learned model103according to the present exemplary embodiment sets a target velocity x1 as input data, and a phase difference z1 and a frequency z2 as output data. The second learned model107sets a positional deviation x2 as input data, and a phase difference z1 and a frequency z2 as output data. The hidden layer is formed of seven different neurons. A common sigmoid function (seeFIG.3B) was used as an activation function. The number of neurons in the hidden layer is not limited to 7. The preferred number of neurons is, for example, 3 to 20. The smaller number of neurons provides faster learning with a lower accuracy, and the larger number of neurons provides a higher accuracy with slower learning. Typically, a sigmoid function or a ReLU (ramp function) is used as the activation function of the output layer. However, a linear function (FIG.3C) was used to cope with the negative sign of the phase difference as a control amount.

FIG.3Aillustrates a weight wh that connects the neurons of the input and the hidden layers, a threshold value θh of the neuron of the hidden layer, a weight wo that connects the neurons of the hidden and the output layers, and a threshold value θo of the neuron of the output layer. All of the weights and threshold values are applied with values learned by the machine learning unit12. A learned NN can be grasped as an aggregate as a result of extracting common feature patterns from time-series data of the velocity and control amounts of the vibration type actuator. The output value is obtained by a function having weights and threshold values as parameters.

Although the present exemplary embodiment applies the phase difference and frequency as control amounts, a combination of the pulse width and frequency or a combination of the pulse width and phase difference is also applicable. The output layer of the NN may include one neuron, and may be designed so that either one of the phase difference, frequency, and pulse width is selected.

An example of a vibration type actuator applicable to the present embodiment will now be described with reference to the accompanying drawings. The vibration type actuator according to the present exemplary embodiment includes a vibrator and a contact member.FIGS.4A to4Dillustrate a drive principle of a linear drive (linear motion) vibration type actuator as an example of a vibration type actuator. The vibration type actuator13illustrated inFIG.4Aincludes an elastic member203, a vibrator131, and the contact member132driven by the vibrator131. The vibrator131includes a piezoelectric element204as an electro-mechanical energy converter adhesively bonded to the elastic member203. When the piezoelectric element204is applied with an AC voltage, the vibrator131generates two different vibration modes as illustrated inFIGS.4C and4D, and moves the contact member132in pressure contact with protruding members202in the directions of the arrow.

FIG.4Billustrates an electrode pattern of the piezoelectric element204. For example, two different electrode regions having an equal longitudinal size are formed on the piezoelectric element204of the vibrator131. The two electrode regions have the same polarization direction (positive direction). Referring toFIG.4B, the electrode region on the right-hand side of the two electrode regions of the piezoelectric element204is applied with an AC voltage VB, and the electrode region on the left-hand side of the two electrode regions of the piezoelectric element204is applied with an AC voltage VA.

Assume a case where the AC voltages VB and VA have frequencies in the vicinity of the resonance frequency of the first vibration mode, and are in the same phase. At a certain moment, the entire piezoelectric element204(two electrode regions) expands. At another moment, the entire piezoelectric element204(two electrode regions) contracts. As a result, a vibration of the first vibration mode illustrated inFIG.4Coccurs in the vibrator131(hereinafter this vibration is also referred to as a “thrust-up vibration”). Thus, a displacement in the thrust-up direction (Z direction) occurs in the protruding members202.

Assume another case where the AC voltages VB and VA have frequencies in the vicinity of the resonance frequency of the second vibration mode, and are out of phase by 180 degrees. At a certain moment, the electrode region on the right-hand side of the piezoelectric element204contracts, and the electrode region on the left-hand side of the piezoelectric element204expands. At another moment, the electrode region on the right-hand side of the piezoelectric element204expands, and the electrode region on the left-hand side of the piezoelectric element204contracts. As a result, a vibration of the second vibration mode, as illustrated inFIG.4D, occurs in the vibrator131(hereinafter this vibration is also referred to as a “feeding vibration”). Thus, a displacement in the drive direction (feeding direction or X direction) occurs in the protruding members202.

Applying two different AC voltages having frequencies in the vicinity of the resonance frequency of the first and the second vibration modes to the electrodes of the piezoelectric element204therefore enables excites a vibration as a combination of the first and the second vibration modes.

By combining the two vibration modes in such a way, the protruding members202perform an elliptic motion in the section perpendicularly intersecting with the Y direction (direction perpendicularly intersecting with the X and the Z directions) illustrated inFIG.4D. This elliptic motion drives the contact member132in the directions of the arrow illustrated inFIG.4A. The direction in which the contact member132and the vibrator131relatively moves, i.e., the direction in which the contact member132is driven by the vibrator131(X direction in this case) is referred to as a drive direction.

The ratio R of the amplitude of the second vibration mode to the amplitude of the first vibration mode (amplitude of feeding vibration divided by amplitude of thrust-up vibration) can be changed by changing the phase difference between the 2-phase AC voltages input to the two electrodes with an equal size. This vibration type actuator changes the velocity of the contact member132by changing the ratio of the vibration amplitudes.

Although the above descriptions have been made centering on an example of a case where the vibrator131stands still (fixed) and the contact member132moves (driven), the present embodiment is not limited thereto. With the contact member132and the vibrator131, the positions of contact portions need to change relative to each other. For example, it is also possible that the contact member132stands still (fixed) and the vibrator131moves (driven). More specifically, according to the present embodiment, “drive” means changing the position of the contact member132relative to the vibrator131. The absolute position of the contact member132(the position of the contact member132with respect to the position of the housing containing the contact member132and the vibrator131) does not necessarily need to change.

The above descriptions have been made centering on a linear drive (linear motion) vibration type actuator as an example. In other words, the above descriptions have been made centering on an example of a case where the vibrator131or the contact member132moves (driven) in a linear direction. However, the present embodiment is not limited thereto. With the contact member132and the vibrator131, the positions of contact portions may change relative to each other. For example, the vibrator131and the contact member132may move in the rotational direction. Examples of vibration type actuators in which a vibrator and a contact member move in the rotational direction include a ring (rotary) vibration type actuator having a ring-shaped vibrator.

Example applications of a vibration type actuator include automatic focus drive of a camera.

FIG.5illustrates a lens drive mechanism of a lens barrel. A lens holder drive mechanism based on a vibration type actuator includes a vibrator, a lens holder, and a first and a second guide bars parallelly disposed to slidably support the lens holder. The present exemplary embodiment will be described below centering on a case where the second guide bar as a contact member is fixed to other members of the lens barrel, and the vibrator and the lens holder integrally move.

The vibrator produces a relative moving force between the vibrator and the second guide bar in contact with the protruding members of the vibrator (elastic member) by the elliptic motion of the protruding members of the vibrator (elastic member) generated by applying the drive voltage to the electro-mechanical energy converter. The lens holder integrally fixed with the vibrator is thereby configured to move along the first and the second guide bars.

More specifically, a drive mechanism300of the contact member132mainly includes a lens holder302as a lens support member, a lens306, the vibrator131with a flexible printed circuit board bonded thereto, and a pressure magnet305. The drive mechanism300further includes two different guide bars (a first guide bar303and a second guide bar304), and a base member (not illustrated). The vibrator131as an example of a vibrator will be described here.

The first guide bar303and the second guide bar304are fixedly supported at both ends of the bars (the first guide bar303and the second guide bar304) by a base member (not illustrated) so that the bars are disposed parallel to each other. The lens holder302includes a cylindrical holding member302a, a support member302bfixedly supporting the vibrator131and the pressure magnet305, and a first guide member302cacting as a guide with the first guide bar303fit thereinto.

The pressure magnet305configured to apply pressure includes a permanent magnet, and two different yokes disposed at both ends of the permanent magnet. A magnetic circuit is formed between the pressure magnet305and the second guide bar304, and an attractive force occurs between these members (the pressure magnet305and the second guide bar304).

The pressure magnet305and the second guide bar304are disposed across a space therebetween. The second guide bar304is disposed in contact with the vibrator131.

The above-described attractive force applies a pressure between the second guide bar304and the vibrator131. The two protruding members202of the elastic member203are in pressure contact with the second guide bar304to form a second guide member. The second guide member forms a guide mechanism by using the attractive force generated by the magnetism. If the second guide member is applied with an external force, the vibrator131and the second guide bar304are separated. To prevent this separation, the following measures are taken.

More specifically, since a dropout prevention member302ddisposed on the lens holder302comes in contact with the second guide bar304, the lens holder302returns to a desired position.

When the vibrator131is applied with a predetermined AC voltage (AC signal), a driving force occurs between the vibrator131and the second guide bar304to drive the lens holder302.

The actual position (detected position) and the actual velocity (detected velocity) are detected by a position sensor (not illustrated) attached to the contact member132or the vibrator131. The actual position (detected position) is fed back to the control unit10as a positional deviation. The vibration type actuator is thereby subjected to feedback control so that the actual position follows the target position for each unit time. The actual velocity (detected velocity) is input to the machine learning unit12to be used as learning data together with the control amounts (phase difference and frequency) output from the control unit10. The learning data includes a pair of input data and output data (correct answer data). According to the present exemplary embodiment, the learning data includes a pair of the detected velocity as input data and control amounts (phase difference and frequency) as output data (correct answer data).

The present exemplary embodiment will be described below centering on a 2-phase drive control apparatus that drives a piezoelectric element as an electro-mechanical energy converter on a 2-phase basis. However, the present exemplary embodiment is not limited to 2-phase drive, and is also applicable to a vibration type actuator with two or more phases.

The machine learning unit12will now be described in detail. A control amount output unit having a learning model includes an NN structure (seeFIGS.3A to3C) to which the actual velocity (detected velocity) from the velocity detection unit16and the target deviation are input, and from which the phase difference and frequency are output. The target deviation refers to a value set to be followed by the positional deviation when the contact member132is moved relative to the vibrator131.

The target deviation has the same data form as the positional deviation. Although the target deviation is set to zero, for example, an offset value may be applied to compensate for backlash of a mechanical system.

The control amounts (phase difference and frequency) output from the control unit10are used as correct answer data. The machine learning unit12compares such control amounts with the control amounts output from the control amount output unit having an unlearned learning model or a learning model currently being learning, to calculate errors. Although the present exemplary embodiment uses the phase difference and frequency as control amounts, a combination of the pulse width and frequency, and a combination of the pulse width and phase difference can also be used as control amounts. The output layer of the NN may include one neuron, and may be designed such that either one of the phase difference, frequency, and pulse width is selected. In the following descriptions, a “control amount output unit having a learning model” is also simply referred to as a “learning model”.

FIG.6is a control flowchart based on machine learning and a learned model according to the present exemplary embodiment. In step S1, the machine learning unit12sets the weights and threshold values of the first learned model103and the second learned model107of the control unit10to the initial values. Although the initial values are set based on a random function (unlearned state), previously learned parameters may be set. In step S2, the machine learning unit12controls the vibration type actuator by using the unlearned model (unlearned NN).

In step S3, the machine learning unit12acquires, as learning data, the addition value of the control amounts output from the first learned model103and the second learned model107during the drive of the vibration type actuator. Time-series data of the actual velocity (detected velocity) detected by the velocity detection unit16and the positional deviation (shift amount) is acquired as learning data during the drive of the vibration type actuator. In step S4, the machine learning unit12performs the optimization operation of the learning model based on the machine learning by using the control amounts of the learning data as correct answer data. The optimization refers to adjusting the parameters of the NN such that the output from the NN by the input to the NN is approximated to the learning data, and is not limited to adjusting the parameters of the NN such that the output from the NN by the input to the NN coincides with the learning data.

The weights and threshold values of the NN are thus optimized by the machine learning, and the parameters of the first learned model103and the second learned model107are updated. The machine learning unit12has a program that causes a computer (not illustrated) to execute these steps S1to S4. In step S5, the machine learning unit12controls the vibration type actuator by using the learned models (the first learned model103and the second learned model107) having the updated weights and threshold values.

After the control, the processing returns to step S3to cope with changes of drive conditions or temperature environment. In step S3, the machine learning unit12acquires the learning data. To acquire the learning data, the machine learning unit12implements batch learning in which learning is performed during the drive deactivation, or on-line learning in which successive learning is performed during the drive.

FIGS.7A and7Bare timing charts illustrating batch learning and on-line learning (manufacturing methods of the vibration type actuator control apparatus) performed by the machine learning unit12. The horizontal axis is assigned time, and the vertical axis is assigned the target position to be given as a command value to subject the vibration type actuator to feedback control.

FIG.7Aillustrates an example of the batch learning in which learning is performed during the drive deactivation (cases other than a case of moving the contact member132relative to the vibrator131). The present exemplary embodiment acquires, as learning data, the time-series data of the detected velocity detected during the drive period of the vibration type actuator and the control amounts, and updates the parameters (weights and threshold values) of the machine learning and the NN by using the deactivation period. However, the machine learning may not be performed for each deactivation period. For example, it is possible to perform learning only when a change of the temperature environment or drive conditions is detected.

FIG.7Billustrates an example of the on-line learning in which successive learning is performed during the drive (a case of moving the contact member132relative to the vibrator131). The present exemplary embodiment performs the on-line machine learning in parallel with the drive period of the vibration type actuator, and updates the parameters (weights and threshold values) of the NN during the drive period. Applying the on-line learning also enables coping with load variations occurring during the drive period.

The above-described machine learning in step S4will be descried with reference toFIGS.8A to8C.FIG.8Ais a flowchart illustrating Adam as a parameter optimization method (optimization algorithm) for the NN. Steps S1and S2are as described above with reference toFIG.6.

In step S3, the machine learning unit12acquires the control amount (n) and the detected velocity (n) as time-series learning data illustrated inFIG.8B. Although the present exemplary embodiment will be described centering on the first learning unit110, the second learning unit111performs similar procedures and redundant descriptions thereof will be omitted. The detection velocity (n) and the control amount (n) are measurement data when the vibration type actuator13is controlled based on an unlearned model. The detected velocity (n) refers to the velocity detected by the velocity detection unit16when the vibration type actuator13is driven based on the control amount (n). The sample count n is 3,400 for each of the detection velocity and the control amount (phase difference). This is measurement data when the vibration type actuator13is driven for 0.34 seconds at a control sampling rate (1/control sampling interval) of 10 kHz.

The learning data may not be acquired at the control sampling rate. Thinning out sampling leads to saving the memory and shortening the learning time. In the present embodiment, the machine learning unit12inputs the detected velocity (n) as the input to a learning model and acquires an output z(n) as a result of the operation (derivation) based on the learning model. The machine learning unit12then compares the output z(n) with the control amount (n) as the correct answer data of the learning data to calculate an error e(n).

More specifically, the error e(n)=(z(n)−control amount (n))2. In step S4, the machine learning unit12calculates an error E (=Σe(n)=Σ(z(n)−control amount (n))2) for 3,400 samples in the first loop, and calculates an error gradient ∇E of each of weights (wh and wo) and threshold values (θh and θo).

By using the error gradient ∇E, the machine learning unit12performs the following parameter optimization through Adam as an optimization operation method (optimization algorithm).

Parameter values η=0.001, β1=0.9, β2=0.999, and ε=10e-12 were used. Each time the optimization operation is repeated, the weights and the threshold values are updated, and the output z(n) of the learning model is approximated to the control amount (n) of the correct answer data, thus decreasing the error E.

FIG.8Cillustrates a transition of the error E based on the operation loop count. Other techniques (algorithms) are also applicable as an optimization method (optimization algorithm).

FIG.9Aillustrates a comparison of operation results of Adam, RMSprop, Momentum, and SGD based on the learning model and measured learning data according to the present exemplary embodiment. From the viewpoint of the number of operations, stability, and final error, the most excellent result was obtained with Adam.

FIG.9Billustrates a learning example of the control amount (phase difference) by Adam.FIG.9Billustrates a state where the output z of the learning model in the first loop is largely different from the control amount t of the correct answer data. After the operation is repeated 5,000 times (loop count=5,000), the output z of the learning model substantially coincides with the control amount t of the correct answer data. Although, in this learning example, the machine learning unit12performs the optimization with a loop count of 5,000, it is desirable to suitably adjust the loop count according to the convergence rate.

The above completes the description of the control apparatus according to the present embodiment. The control unit10and the machine learning unit12include, for example, a central processing unit (CPU), digital devices such as a programmable logic device (PLD) including an application specific integrated circuit (ASIC), and elements such as an analog-to-digital (A/D) converter. The AC signal generation unit104of the drive unit11includes, for example, a CPU, a function generator, and a switching circuit. The booster circuit of the drive unit11includes, for example, coils, a transformer, and capacitors. Each of the control unit10, the machine learning unit12, and the drive unit11may include not only one element or circuit but also a plurality of elements or circuits. Each piece of processing in the control unit10, the machine learning unit12, and the drive unit11may be performed by any element or circuit.

Results of the applying the control according to the present embodiment to an actual machine will be described below.

FIGS.10A and10Billustrate results of the feedback control on the vibration type actuator13performed by the control apparatus according to the present embodiment, based on a predetermined target position pattern.

The target velocity relates to a pattern in which a reciprocal operation is performed by the trapezoid drive at up to 50 mm/s, with a 5 mm stroke including the positioning operation. In the charts, the horizontal axis is assigned time (second). In the top charts, the vertical axis is assigned the velocity (mm/s). In the bottom charts, the vertical axis on the left-hand side is assigned the target position (with an encoder pulse count of 8,000 pls per mm), and the vertical axis on the right-hand side is assigned the positional deviation in μm (micrometers).

FIG.10Aillustrates results of proportional-integral-derivative (PID) control according to a comparative example.FIG.10Billustrates results of PID control according to the present embodiment. The starting frequency was set to 93 kHz in both cases. Referring to the results in the top charts, the detected velocity substantially coincides with the target velocity, and favorable traceability is obtained. The detected velocity and the velocity deviation according to the present embodiment are slightly noisy because of differences in filter processing. This phenomenon is not related to the purpose of the present embodiment and therefore may be ignored. Referring toFIG.10A, “Target Velocity and Detected Velocity” appears as a single line, it includes both the line of the target velocity and the line of the detected velocity.

The bottom charts representing the traceability of the detected velocity with respect to the target position will now be described. The bottom charts illustrate that the present embodiment has largely improved the positional deviation in the PID control. This effect is obtained by learning the velocity characteristics for the control amounts of the vibration type actuator.

FIG.11illustrates a result indicating the robustness in the vibration type actuator control apparatus according to the present embodiment.

This chart illustrates a result of performing the positioning operation by the trapezoidal drive at a maximum velocity of 50 mm/s and calculating the positional deviation during the 5 mm stroke reciprocal operation with36. The horizontal axis is assigned the starting frequency, and the vertical axis is assigned the positional deviation. The result of the comparative example is obtained by the conventional PID control. According to the control result of the present embodiment, the positional deviation has been largely improved in comparison with the comparative example. The chart illustrates that the positional deviation changes small even if the drive frequency changes, meaning that the robustness has been improved by the present embodiment.

As described above, it has been hard for the conventional PID control to cope with load variations since performing control with different starting frequencies causes variations of the velocity gradient by the non-linear characteristics of the vibration type actuator. According to the present embodiment, the control amounts according to the target velocity are output from the first learned model103. The control deviation arising from drive conditions or temperature environment can be supplemented by the control amounts output from the second learned model107, thus implementing high-precision and high-robustness control.

Other exemplary embodiments of the present disclosure will now be described.

FIG.12illustrates a vibration type actuator control apparatus according to the second exemplary embodiment of the present disclosure.

The present embodiment performs the machine learning by using the pulse width and frequency as control amounts, and performs control by using such learned model. In such a control block, the position feedback control for the vibration type actuator13is performed based on the addition value of the control amounts output from a first learned model1003and a second learned model1007, the pulse width, and the frequency. The machine learning unit12acquires, as learning data, the control amounts (pulse width and frequency) output from the control unit10and the relative velocity detected by the velocity detection unit16, and performs the machine learning for the first learned model1003. The machine learning unit12also acquires the differential values of the control amounts and the positional deviation as learning data, and performs the machine learning for the second learned model1007.

FIGS.13A and13Billustrate the velocity characteristics of the vibration type actuator13based on the control amounts.

FIG.13Aillustrates control using the phase difference and frequency (refer to the first exemplary embodiment). The horizontal axis is assigned the frequency, and the vertical axis is assigned the motor speed. As illustrated in13A, the motor speed is controlled by operating each of the phase difference and frequency. When control is performed in the gray region, for example, each control amount is output with a drive frequency from 88 to 93 kHz and a phase difference from 0 to ±120 degrees. The first learned model103according to the present embodiment outputs two different control amounts in response to an input of the target velocity.

FIG.13Billustrates control using the pulse width and frequency according to the present exemplary embodiment. Likewise, the motor speed can be controlled by operating each of the pulse width and frequency. For example, when control is performed in the gray region, each control amount is output with a drive frequency from 88 to 93 kHz and a pulse width from 0 to 50%. Likewise, the first learned model1003outputs two different control amounts in response to the target velocity input to the first learned model1003.

A third exemplary embodiment of the present disclosure will now be described.

FIG.14is a control block diagram illustrating a vibration type driving apparatus in a case of control using a learned model that completed the machine learning by using the phase difference, frequency, and pulse width as control amounts. Referring toFIG.14, the portion excluding the vibration type actuator13from the vibration type driving apparatus17forms the control apparatus15. In such a control block, the position feedback control for the vibration type actuator13is performed based on each of the control amounts (pulse width and frequency) output from each of the first learned model1103and the second learned model1107. More specifically, the control is performed by using the addition value of the control amounts output from the first learned model1103and the control amounts output from the second learned model1107. The addition of the control amounts is performed between the control amounts of the same types. The target velocity is input to the first learned model1103which then outputs the phase difference, frequency, and pulse width having been subjected to the operation by the NN, to the drive unit11. The positional deviation is input to the second learned model1107which then outputs the phase difference, frequency, and pulse width having been subjected to the operation by the NN configuring the second learned model1107, to the drive unit11.

The machine learning unit12acquires, as learning data, the three control amounts output from the control unit10and the relative velocity (detected velocity) detected by the velocity detection unit16, and performs the machine learning for the first learned model1103. The machine learning unit12also acquires the differential values of the control amounts and the positional deviation as learning data, and performs the machine learning for the second learned model1107.

FIG.15illustrates the NN structure that outputs the phase difference, frequency, and pulse width.

The first learned model1103and the second learned model1107has an NN structure to which the target velocity or the positional deviation is input, and the NN structure outputs the three control amounts. The learning data to be used for the machine learning may be measurement data in a case of controlling the vibration type actuator by a learned model, or measurement data in a case of controlling the vibration type actuator by an unlearned model with parameters set by a random function. In addition, measurement data in a case of controlling the vibration type actuator by the open drive, or measurement data in a case of controlling the vibration type actuator by PID control.

When the weights and threshold values of the NN are determined, parameters having conditions most suitable from the viewpoint of the positional deviation or power consumption may be selected from a plurality of learning data pieces. This is because conditions for obtaining a predetermined velocity of the vibration type actuator, i.e., combinations of the phase difference, frequency, and pulse width innumerably exist.

Since applying the present exemplary embodiment increases the number of parameters for operating the vibration type actuator, performing suitable machine learning enables fine adjustment of the control performance.

FIG.16illustrates a vibration type actuator control apparatus according to a fourth exemplary embodiment of the present disclosure.

In such a control block, the position feedback control for the vibration type actuator13is performed by connecting a PID controller109in parallel with the second learned model1207. The positional deviation is input to the PID controller109which then outputs the control amounts (phase difference and frequency) having been subjected to the PID operation.

The configuration is not limited to a PID controller. For example, proportional (P) control, proportional-integral (PI) control, and proportional-derivative (PD) control are also applicable. The target velocity is input to a first learned model1203. The PID control amount (third control amount) output from the PID controller109, the first control amount output from the first learned model1203, and the second control amount output from the second learned model1207are added and then output to the drive unit11. The machine learning unit12acquires, as learning data, the control amounts (phase difference and frequency) output from the control unit10and the relative velocity detected by the velocity detection unit16, and performs the machine learning for the first learned model1203. The machine learning unit12also acquires the differential values of the control amounts and the positional deviation as learning data, and performs the machine learning for the second learned model1207.

According to the present exemplary embodiment, the control amounts according to the target velocity are output by the first learned model1203. The control deviation arising from drive conditions and temperature environment can be supplemented by the control amounts output from the second learned model1207, thus implementing high-accuracy and high-robustness control. Using the PID controller109in parallel enables flexibly adjusting the transfer characteristics of the control loop, and further improving the positioning accuracy and robustness.

FIG.17illustrates a vibration type actuator control apparatus according to a fifth exemplary embodiment of the present disclosure.

In such a control block, the PID controller109is connected in parallel with a second learned model1207for control, and the position feedback control of the vibration type actuator13is selectively performed through switches (SWs). A SW1turns the output of the PID controller109ON or OFF. A SW2turns the output of the addition value of the first and the second control amounts ON or OFF. This configuration thus switches between control only by the PID controller109and control only by the learned models1603and1607according to conditions.

Control may be performed with both switches turned ON. This also applies to the machine learning. For example, performing the learning only by the PID controller109enables stably generating a learned model even in a state where the learning has not been performed.

Applying the present exemplary embodiment implements high-accuracy and high-robustness control.

Selectively using the PID controller109also enables improving the stability of control and learning.

According to the above-described exemplary embodiments, there may be provided a storage unit that stores parameters (a first weight, a second weight, a threshold value of a second neuron, and a threshold value of a third neuron) included in the NN. The NN may be subjected to the machine learning when the parameters included in the NN are replaced by the parameters stored in the storage unit.

The above-described exemplary embodiments may include environmental sensors that detect environmental conditions. The NN may be subjected to the machine learning when the environmental sensors detect environmental changes. The environmental sensors may include at least either one of a temperature sensor or a humidity sensor.

Although the first exemplary embodiment has been described above centering on an example where the vibration type actuator control apparatus is used to drive the automatic focus lens of the imaging apparatus, example applications of the present embodiment are not limited thereto. For example, as illustrated inFIGS.18A and18B, the control apparatus can also be used to drive the lens and an image sensor at the time of camera shake correction.FIG.18Ais a plan view (top view) illustrating an outer appearance of an imaging apparatus60.FIG.18Bis a schematic view illustrating an internal structure of the imaging apparatus60.

The imaging apparatus60generally includes a main body61, and a lens barrel62attachable to and detachable from the main body61. The main body61includes an image sensor63, such as a charge coupled device (CCD) sensor or complementary metal oxide semiconductor (CMOS) sensor, configured to convert an optical image formed by light that passes through the lens barrel62into an image signal, and a camera control microcomputer64that controls the entire operations of the imaging apparatus60. The lens barrel62includes a plurality of lenses L, such as a focusing lens and a zoom lens, disposed at predetermined positions.

The lens barrel62includes an image shake correction apparatus50that includes a disc member56, and the vibrator131disposed on the disc member56. An image shake correction lens65is disposed in a hole formed at the center of the disc member56. The image shake correction apparatus50is disposed to enable the image shake correction lens65to move in a plane perpendicularly intersecting with the optical axis of the lens barrel62. In this case, the vibration type actuator control apparatus15according to the present embodiment drives the vibrator131, and thereby the vibrator131and the disc member56moves relatively to the contact member132fixed to the lens barrel62, thus driving the image shake correction lens65.

The vibration type actuator control apparatus15according to the present embodiment can also be used to drive the lens holder302that moves the zoom lens. Thus, the control apparatus15according to the present embodiment is mounted not only on an imaging apparatus but also on an interchangeable lens for lens drive.

The vibration type actuator control apparatus15according to the first exemplary embodiment is also used to drive an automatic stage. For example, the control apparatus15is also used to drive the automatic stage of a microscope, as illustrated inFIG.19.

The microscope illustrated inFIG.19is composed of an imaging unit70including an image sensor and an optical system, and an automatic stage71including a stage72moved by a vibration type actuator, mounted on a base. The imaging unit70captures a magnified image of an object under observation placed on the stage72. When the observation range is wide, the stage72is moved by driving the vibration type actuator by using the vibration type actuator control apparatus15according to the first or the second exemplary embodiment. This enables moving the object under observation in the X and Y directions to acquire a number of captured images. Using a computer (not illustrated) enables combining captured images to acquire one high-definition image with a wide observation range.

The present disclosure makes it possible to provide a vibration type actuator control apparatus having a control amount output unit different from the conventional PID controller, as a main control amount output unit.

Other Embodiments

This application claims the benefit of Japanese Patent Application No. 2021-033662, filed Mar. 3, 2021, which is hereby incorporated by reference herein in its entirety.