WO2023074163A1 - 制御パラメータの生成方法、プログラム、記録媒体、および、制御パラメータの生成装置 - Google Patents

制御パラメータの生成方法、プログラム、記録媒体、および、制御パラメータの生成装置 Download PDF

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Publication number
WO2023074163A1
WO2023074163A1 PCT/JP2022/034357 JP2022034357W WO2023074163A1 WO 2023074163 A1 WO2023074163 A1 WO 2023074163A1 JP 2022034357 W JP2022034357 W JP 2022034357W WO 2023074163 A1 WO2023074163 A1 WO 2023074163A1
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Prior art keywords
control parameter
driven
evaluation index
index data
control
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English (en)
French (fr)
Japanese (ja)
Inventor
俊策 利弘
弘 藤原
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Panasonic Intellectual Property Management Co Ltd
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Panasonic Intellectual Property Management Co Ltd
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Priority to JP2023556182A priority Critical patent/JP7557682B2/ja
Priority to CN202280071309.2A priority patent/CN118140182A/zh
Priority to KR1020247017430A priority patent/KR20240090974A/ko
Priority to US18/703,312 priority patent/US20240411276A1/en
Priority to EP22886497.1A priority patent/EP4425279A4/en
Publication of WO2023074163A1 publication Critical patent/WO2023074163A1/ja
Anticipated expiration legal-status Critical
Priority to JP2024139256A priority patent/JP7738227B2/ja
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Program-control systems
    • G05B19/02—Program-control systems electric
    • G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of program data in numerical form
    • G05B19/19—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of program data in numerical form characterised by positioning or contouring control systems, e.g. to control position from one programmed point to another or to control movement along a programmed continuous path
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Program-control systems
    • G05B19/02—Program-control systems electric
    • G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of program data in numerical form
    • G05B19/404—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of program data in numerical form characterised by control arrangements for compensation, e.g. for backlash, overshoot, tool offset, tool wear, temperature, machine construction errors, load, inertia
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

Definitions

  • the present disclosure relates to a control parameter generation method, a program, a recording medium, and a control parameter generation device, and more particularly to a control parameter generation method, a program, a recording medium, and a control parameter generation device used in a production apparatus. .
  • Patent Document 1 a method for generating control parameters for a motor and a drive machine using a machine learning model has been proposed for a drive machine using a motor (see Patent Document 1, for example).
  • an object of the present disclosure is to provide a control parameter generation method, a program, a recording medium, and a control parameter generation device that can generate appropriate control parameters.
  • a control parameter generation method includes a servo motor, a control circuit that controls the servo motor, a memory that stores control parameters used by the control circuit to control the servo motor, and A drive target driven by a servomotor, and a control parameter generation method for use in a production apparatus, comprising the following actions. That is, after the time when the object to be driven reaches an allowable position at which it can be evaluated that the object to be driven has reached the predetermined target position based on a command to move the position of the object to be driven to a predetermined target position, the position of the object to be driven is Measured data representing the position is obtained from a sensor that measures the position.
  • evaluation index data representing the vibration of the driven object after the arrival time is generated.
  • control parameters are updated using a machine learning model that learns the relationship between the evaluation index data and the control parameters.
  • the updated control parameters are output to the production equipment for storage in the memory.
  • a program according to one aspect of the present disclosure is a program that causes a computer of an information processing device connected to the production device to execute the control parameter generation method.
  • a recording medium is a recording medium recording a program for causing a computer of an information processing device connected to the production device to execute the control parameter generation method.
  • a control parameter generation device includes a servo motor, a first control circuit that controls the servo motor, and a control that the first control circuit uses when controlling the servo motor.
  • a control parameter generation device for use in a production device comprising a memory for storing parameters and a driven object driven by the servomotor.
  • This control parameter generator includes an input unit and a second control circuit. The input unit controls the driving object after reaching an allowable position at which it can be evaluated that the driving object has reached the predetermined target position based on a command to move the driving object to a predetermined target position. Measurement data representing the position of the object is obtained from a sensor that measures the position of the object.
  • the second control circuit generates evaluation index data representing the vibration of the driven object after the arrival time based on the measurement data, and based on the evaluation index data, generates the evaluation index data and the A machine learning model that learns relationships to control parameters is used to update the control parameters.
  • the output unit includes an output unit that outputs the updated control parameter to the production device.
  • a control parameter generation method includes a servo motor, a control circuit that controls the servo motor, and a memory that stores control parameters used by the control circuit to control the servo motor.
  • the method for generating control parameters for use in a production apparatus comprising the following actions. That is, after the time at which the object to be driven reaches the allowable position at which it can be evaluated that the object to be driven has reached the predetermined target position based on the command for setting the position of the object to be driven to the predetermined target position, the object to be driven is Evaluation index data representing the vibration of the driven object after the arrival time is obtained from a sensor with a processing device that measures the position. Based on the evaluation index data, the control parameters are updated using a machine learning model that learns the relationship between the evaluation index data and the control parameters.
  • a program according to another aspect of the present disclosure is a program that causes a computer of an information processing device connected to the production device to execute the control parameter generation method.
  • a recording medium is a recording medium recording a program for causing a computer of an information processing device connected to the production device to execute the control parameter generation method.
  • a control parameter generation device includes a servo motor, a first control circuit that controls the servo motor, and a control that the first control circuit uses when controlling the servo motor.
  • a control parameter generation device for use in a production device comprising a memory for storing parameters and a driven object driven by the servomotor.
  • the control parameter generation device includes an input unit and a second control circuit.
  • the input unit controls the driving object after reaching an allowable position at which it can be evaluated that the driving object has reached the predetermined target position based on a command to move the driving object to a predetermined target position.
  • Evaluation index data representing the vibration of the driven object after the arrival time is acquired from a sensor with a processing device that measures the position of the object.
  • a second control circuit that updates the control parameter using a machine learning model that learns a relationship between the evaluation index data and the control parameter based on the evaluation index data; Prepare.
  • control parameter generation method program, recording medium, and control parameter generation device according to the above aspects of the present disclosure, it is possible to generate appropriate control parameters.
  • FIG. 1 is a schematic diagram showing an outline of a control parameter generation system according to Embodiment 1.
  • FIG. 2 is a block diagram showing the configuration of the control parameter generation system according to Embodiment 1.
  • FIG. 3 is a schematic diagram showing an example of control parameters according to the first embodiment.
  • FIG. 4 is a schematic diagram showing an example of the transition of the positional deviation of the driven object with respect to the target position.
  • FIG. 5 is a schematic diagram showing an example of measurement data according to Embodiment 1.
  • FIG. FIG. 6 is a schematic diagram showing how the machine learning model 14 according to Embodiment 1 outputs the control parameters based on the result of learning the relationship between the evaluation index data and the control parameters.
  • FIG. 7 is a diagram illustrating an example of an image generated by an image generating unit according to Embodiment 1.
  • FIG. 8 is a diagram illustrating an example of an image generated by an image generation unit according to Embodiment 1.
  • FIG. 9 is a sequence diagram of the first control parameter adjustment process according to Embodiment 1.
  • FIG. 10 is a flowchart of a first control parameter adjustment process according to Embodiment 1.
  • FIG. 11 is a schematic diagram showing an overview of a control parameter generation system according to Embodiment 2.
  • FIG. 12 is a block diagram showing the configuration of a control parameter generation system according to Embodiment 2.
  • FIG. 13 is a sequence diagram of second control parameter adjustment processing according to the second embodiment.
  • FIG. 14 is a flowchart of second control parameter adjustment processing according to the second embodiment.
  • FIG. 15 is a block diagram showing the configuration of a generation device according to a modification.
  • control parameters used in a production apparatus having a servomotor for driving an object to be driven has been performed manually.
  • the number of control parameters used in such production equipment can reach as many as 80, and the number of parameters that actually need to be adjusted can reach as many as 30-60.
  • the parameters to be adjusted differ depending on the driven object to be mounted on the target production apparatus, the application, and the like.
  • Patent Document 1 proposes adjusting such control parameters using a machine learning model.
  • a skilled engineer adjusts the control parameters using the five human senses, such as confirming abnormal noise with hearing and confirming vibration with sight and touch. Therefore, it is necessary to separately consider what kind of data should be input to the machine learning model to effectively adjust the control parameters.
  • the evaluation index data may indicate the degree of variation in the deviation waveform indicating the vibration of the driven object after the arrival time.
  • the production equipment may include any one of mounting equipment, processing equipment, or machining equipment.
  • a control parameter generation method includes a servo motor, a control circuit that controls the servo motor, and a memory that stores control parameters used by the control circuit to control the servo motor.
  • the method for generating control parameters for use in a production apparatus comprising the following actions. That is, after the time when the object to be driven reaches an allowable position at which it can be evaluated that the object to be driven has reached the predetermined target position based on a command to move the position of the object to be driven to a predetermined target position, the position of the object to be driven is Evaluation index data representing the vibration of the driven object after the arrival time is obtained from a sensor with a processing device that measures the time. Based on the evaluation index data, the control parameters are updated using a machine learning model that learns the relationship between the evaluation index data and the control parameters.
  • a control parameter generation device includes a servo motor, a first control circuit that controls the servo motor, and a control that the first control circuit uses when controlling the servo motor.
  • a control parameter generation device for use in a production device comprising a memory for storing parameters and a driven object driven by the servomotor.
  • the control parameter generation device includes an input unit and a second control circuit.
  • the input unit controls the driving object after reaching an allowable position at which it can be evaluated that the driving object has reached the predetermined target position based on a command for setting the position of the driving object to a predetermined target position.
  • Evaluation index data representing the vibration of the driven object after the arrival time is obtained from a sensor with a processing device that measures the position of the object.
  • the second control circuit updates the control parameters using a machine learning model that learns the relationship between the evaluation index data and the control parameters based on the evaluation index data.
  • This control parameter generation system is a system for generating control parameters used in a production apparatus having a servomotor for driving an object to be driven.
  • control parameter generation system 1 includes a generation device 10, a production device 20, and a sensor 30.
  • the memory 21 stores control parameters used when the control circuit 22 controls the servomotor 23 .
  • the control parameters stored in the memory 21 are control parameters output from the generation device 10 .
  • control parameters stored in the memory 21 include, for example, parameters a1 and a2 for adjusting the vibration frequency of the driven object 24, parameters b1 and b2 for adjusting the acceleration of the driven object 24, driving Parameters c1 and c2 for adjusting the depth of singularity in the vibration characteristics of the object 24, parameters d1 and d2 for adjusting the vibration amplitude of the driven object 24, and the like are included.
  • FIG. 4 is a schematic diagram showing an example of transition of the positional deviation of the driven object 24 with respect to the target position when the production apparatus 20 drives the driven object 24 to the target position.
  • the allowable position means a position where the positional deviation from the target position is within the required accuracy.
  • the time at which the target position is reached and the allowable position is reached (hereinafter also referred to as "settling time") means that the driven object 24 is at the allowable position. It refers to the time (time sett_t in FIG. 4) when the vehicle finally reaches the allowable position when it does not deviate again from the allowable position after reaching the allowable position.
  • FIG. 5 is a data configuration diagram showing an example of measurement data output by the sensor 30.
  • the measurement data is, for example, data in which the elapsed time [ms] after reaching the allowable position and the amount of deviation [mm] from the target position are associated one-to-one.
  • the generation device 10 generates control parameters (hereinafter also referred to as “update control parameters") for updating control parameters stored in the memory 21 (hereinafter also referred to as “stored control parameters"). More specifically, the generating device 10 sequentially acquires measurement data output from the sensor 30 as a result of the production device 20 driving the driven object 24 to the target position using the stored control parameters, and calculates the acquired measurement data. Update control parameters are sequentially generated based on the data, and the generated update control parameters are sequentially output to the production apparatus 20 .
  • the generation device 10 includes an input unit 11, an output unit 12, a control circuit 13, a machine learning model 14, an operation reception unit 15, an image generation unit 16, and a display unit 17. Prepare.
  • the input unit 11 acquires measurement data output from the sensor 30 .
  • control circuit 13 Based on the measurement data acquired by the input unit 11, the control circuit 13 generates evaluation index data indicating the vibration of the driven object 24 after the settling time, and generates evaluation index data and The control parameters are updated using a machine learning model 14 that learns the relationship with the control parameters.
  • the evaluation index data generated by the control circuit 13 is designated by the user who uses the control parameter generation system 1.
  • the start point sett_t of the integration interval is the settling time
  • the end point cal_e of the integration interval is the time set by the user.
  • the starting point sett_t of the integration interval is assumed to be the settling time, but the starting point sett_t of the integration interval may be the settling time or any time after the settling time.
  • FIG. 6 is a schematic diagram showing how the machine learning model 14 learns the relationship between the evaluation index data and the corresponding control parameters, and outputs the control parameters predicted to minimize the evaluation index data based on the learning result. It is a diagram.
  • the machine learning model 14 uses a Bayesian optimization algorithm to predict the relationship between the evaluation index data and the corresponding control parameter within a certain range, and using the predicted relationship, A control parameter that is predicted to have the smallest evaluation value data is output.
  • the operation accepting unit 15 accepts an input operation to the generating device 10 by the user who uses the control parameter generating system 1 .
  • the display unit 17 displays images provided to the user who uses the control parameter generation system 1.
  • the image generation unit 16 generates an image to be displayed by the display unit 17.
  • FIG. 7 is an example of an image generated by the image generator 16.
  • FIG. FIG. 7 shows how the evaluation index data generated by the control circuit 13 is sent to the user who uses the control parameter generation system 1 when the control parameter generation system 1 starts a first control parameter adjustment process to be described later. It is an example of an image (hereinafter also referred to as an “evaluation index data designation image”) prompting designation of evaluation index data.
  • the generation device 10 displays the evaluation index data designation image on the display unit 17, thereby providing the user with the total area evaluation index data as the evaluation index data generated by the control circuit 13, Any one of variation degree evaluation index data, effective value evaluation index data (hereinafter, these three evaluation index data will also be referred to as “vibration evaluation index data”), and settling time evaluation value data. Prompt to specify at least one.
  • the user designates the settling time evaluation index data as the evaluation index data, for example, by placing a check next to "settling time” for the evaluation index data designation image. For example, the user puts a check mark next to "Vibration” and a circle mark next to "Area” for the evaluation index data designation image, so that the total area evaluation index data is used as the evaluation index data. specify. For example, the user puts a check next to "vibration” and a circle mark next to "dispersion” in the image specifying the evaluation index data, so that the degree of variation evaluation index data is used as the evaluation index data. specify. For example, the user puts a check next to "vibration” and puts a circle mark next to "RMS (rms value)" for the evaluation index data designation image, so that the effective value Specify metric data.
  • RMS rms value
  • the control circuit 13 may use the sum of the value of the settling time evaluation index data and the value of the vibration evaluation index data as the evaluation index data. , vibration evaluation index data, and the sum of the values obtained by multiplying each of the vibration evaluation index data by a predetermined weighting factor.
  • FIG. 8 is another example of an image generated by the image generator 16.
  • FIG. FIG. 8 shows, while the control parameter generation system 1 is executing a first control parameter adjustment process to be described later, for the user using the control parameter generation system 1, evaluation index data in the parameter adjustment process.
  • This is an example of an image that presents the status of values (hereinafter also referred to as a "status presentation image").
  • the horizontal axis represents the number of control parameters sequentially output by the generating device 10
  • the vertical axis represents the evaluation index data value corresponding to each of the control parameters sequentially output by the generating device 10.
  • the solid broken line indicates transition of the minimum values of the evaluation index data corresponding to the control parameters output by the generation device 10 .
  • an icon marked “suspend” (hereinafter also referred to as “suspend icon”) and an icon marked “resume” (hereinafter also referred to as “resume icon”) ) are arranged.
  • the value of the evaluation index value data corresponding to the control parameter output by the generation device 10 is sufficiently small, so that the discontinuation icon is no longer the first control parameter.
  • This is an icon that can be used to interrupt the first control parameter adjustment process by clicking the interruption icon when it is determined that the adjustment process does not need to be continued.
  • the restart icon is displayed because the value of the evaluation index value data corresponding to the control parameter output by the generation device 10 is not sufficiently small, so that the first control parameter adjustment processing is an icon that allows the user to continue the first control parameter adjustment process by clicking the restart icon when it is determined that the first control parameter adjustment process needs to be continued.
  • the control parameter generation system 1 executes a first control parameter adjustment process for adjusting the control parameters stored in the memory 21 to appropriate values.
  • the first control parameter adjustment process is started, for example, when a user using the control parameter generation system 1 performs an operation on the generation device 10 to start the first control parameter adjustment process.
  • FIG. 9 is a sequence diagram of the first control parameter adjustment process
  • FIG. 10 is a flowchart of the first control parameter adjustment process.
  • the generator 10 activates a predetermined program for executing the first control parameter control process (step S10).
  • the output unit 12 When the predetermined program is activated, the output unit 12 outputs the initial values of the control parameters to the memory 21 (step S15). At this time, the output unit 12 may, for example, output an initial value of the control parameter consisting of a predetermined value, or may output an initial value of the control parameter consisting of a value specified by the user. Alternatively, for example, the initial values of the control parameters, which are values calculated by a calculation method specified by the user, may be output.
  • the memory 21 stores the control parameters output from the output unit 12 (step S20). Based on the control parameters stored in the memory 21 , the control circuit 22 then generates a command for setting the position of the driven object 24 to a predetermined target position, and outputs the command to the servomotor 23 . Thereby, the control circuit 22 controls the servo motor 23 (step S25). Then, the servomotor 23 drives the driven object 24 based on the command output from the control circuit 22 (step S30).
  • the sensor 30 measures the position of the driven object 24 (step S35), and reaches an allowable position where it can be evaluated that the driven object 24 has reached a predetermined target position. Measurement data representing the position of the driven object 24 after the arrival time is output to the input unit 11 . Then, the input unit 11 acquires the measurement data output from the sensor 30 (step S40).
  • the control circuit 13 When the input unit 11 acquires the measurement data, the control circuit 13 generates evaluation index data indicating the vibration of the driven object 24 after the settling time based on the measurement data (step S45). Then, the control circuit 13 checks whether the evaluation index data satisfies a predetermined condition (step S50).
  • the predetermined condition is, for example, a condition that the evaluation index data has a value indicating that an appropriate control parameter has been generated.
  • step S50 if the generated evaluation index data does not satisfy a predetermined condition (step S50: No), the control circuit 13 outputs the generated evaluation index data to the machine learning model 14. Then, the machine learning model 14 learns the relationship between the evaluation index data and the corresponding control parameter (step S55), and outputs the control parameter predicted to minimize the value of the evaluation index data. Then, the control circuit 13 updates the control parameters output by the output unit 12 last time with the control parameters newly output from the machine learning model 14 (step S60). Then, the output unit 12 transmits the control parameters newly updated by the control circuit 13 to the memory 21 (step S65).
  • control parameter generation system 1 proceeds to the process of step S20, and repeats the processes of step S20 and subsequent steps.
  • step S50 when the generated evaluation index data satisfies a predetermined condition (step S50: Yes), the control parameter generation system 1 terminates the first control parameter adjustment process.
  • control parameter generation system 1 configured as described above repeatedly adjusts the evaluation index value data by executing the first control parameter adjustment process until the evaluation index value data satisfies a predetermined condition.
  • control parameter generation system 1 proper control parameters can be generated in a relatively short time without relying on a skilled engineer.
  • the control parameter generation system 1 is an example of a system in which the sensor 30 measures the position of the driven object and outputs measurement data, and the measurement data generation device 10 acquires the measurement data output from the sensor 30. rice field.
  • the sensor with processing function according to the second embodiment measures the position of the driven object and outputs evaluation index data.
  • a measurement data generation device is an example of a system that acquires evaluation index data output from the sensor with a processing function according to the second embodiment.
  • control parameter generation system according to Embodiment 2, the same components as in the control parameter generation system 1 have already been described, and the same reference numerals are assigned, and detailed description thereof is omitted. The difference from system 1 will be mainly described.
  • FIG. 11 is a schematic diagram showing an overview of the control parameter generation system 1A according to the second embodiment.
  • FIG. 12 is a block diagram showing the configuration of the control parameter generation system 1A.
  • control parameter generation system 1A is the control parameter generation system 1 according to Embodiment 1, in which the generation device 10 is changed to a generation device 10A, and the sensor 30 is changed to a sensor 30A with a processing function. Configured. Further, as shown in FIG. 12, the generation device 10A is configured by changing the input section 11 to an input section 11A and the control circuit 13 to a control circuit 13A from the generation device 10 .
  • the sensor 30A with processing function measures the position of the driven object 24. Then, the sensor with processing function 30A generates measurement data representing the position of the object to be driven 24 after the time when the object to be driven 24 reaches an allowable position at which it can be evaluated that the object to be driven 24 has reached a predetermined target position. Furthermore, the sensor with processing function 30A generates evaluation index data indicating the vibration of the driven object 24 after the settling time based on the generated measurement data, and outputs the generated evaluation index data to the generating device 10A.
  • the sensor with processing function 30A is, for example, a computer device that includes a sensing device that measures the position of the driven object 24, a processor, a memory, and an input/output interface. is realized by Such a computer device is, for example, a personal computer.
  • the input unit 11A acquires the evaluation index data output from the sensor with processing function 30A.
  • control circuit 13A Based on the evaluation index data acquired by the input unit 11A, the control circuit 13A updates the control parameters using the machine learning model 14 that learns the relationship between the evaluation index data and the control parameters.
  • the control parameter generation system 1A performs a second control parameter adjustment process partially changed from the first control parameter adjustment process according to the first embodiment.
  • FIG. 13 is a sequence diagram of the second control parameter adjustment process
  • FIG. 14 is a flowchart of the second control parameter adjustment process.
  • step S35 is changed from the first control parameter adjustment process to the process of step S135, and the process of step S40 is changed to the process of step S140. , and the process of step S45 is changed to the process of step S145.
  • the sensor with processing function 30A measures the position of the object to be driven 24 (step S135), and determines the time when the object to be driven 24 reaches an allowable position at which it can be evaluated that it has reached a predetermined target position. Measurement data representing the position of the subsequent driven object 24 is generated. Then, the sensor with processing function 30A generates evaluation index data indicating the vibration of the driven object 24 after the settling time based on the generated measurement data (step S140). output to Then, the input unit 11 acquires the evaluation index data output from A, the sensor with processing function 30A (step S145).
  • step S145 When the process of step S145 ends, the control parameter generation system 1A proceeds to the process of step S50.
  • step S50 when the generated evaluation index data satisfies a predetermined condition (step S50: Yes), the control parameter generation system 1A terminates the second control parameter adjustment process.
  • control parameter generation system 1A configured as described above executes the second control parameter adjustment process, similarly to the control parameter generation system 1 according to the first embodiment, so that the evaluation index value data is set to a predetermined value.
  • the evaluation index value data is repeatedly adjusted until the conditions are met.
  • control parameter generation system 1A like the control parameter generation system 1, it is possible to generate appropriate control parameters in a relatively short time without relying on skilled engineers.
  • the generation device 10 is described as being realized by one computer device.
  • the generation device 10 is not necessarily limited to an example implemented by one computer device as long as it can implement similar functions.
  • the generation device 10 may be realized by, for example, a plurality of computer devices that can communicate with each other.
  • FIG. 15 is a block diagram showing an example of the configuration of the generation device 10 when the generation device 10 is realized by a plurality of computer devices that can communicate with each other.
  • the generation device 10 includes (1) a first input unit 11, an output unit 12, an operation reception unit 15, an image generation unit 16, a display unit 17, and a communication unit 18. (2) a second computer device 110 including a control circuit 13, a machine learning model 14, and a communication unit 19; and (3) a communication unit 18 and a communication unit 19 are communicably connected. It may be implemented by a network 120 that
  • the first computer device 100 may be, for example, a personal computer
  • the second computer device 110 may be a server.
  • General or specific aspects of the present disclosure may be implemented in systems, devices, methods, integrated circuits, programs, or non-transitory recording media such as computer-readable CD-ROMs. Also, it may be realized by any combination of systems, devices, methods, integrated circuits, programs, and non-transitory recording media.
  • the present disclosure may be implemented as a program for causing a computer device to execute processing performed by a generation device.
  • the present disclosure can be widely used for systems that generate control parameters.
  • Reference Signs List 1 1A control parameter generation system 10, 10A generation device 11, 11A input unit 12 output unit 13, 13A control circuit 14 machine learning model 15 operation reception unit 16 image generation unit 17 display unit 18, 19 communication unit 20 production device 21 memory 22 control circuit 23 servo motor 24 driven object 30 sensor 30A sensor with processing function 100 first computer device 110 second computer device 120 network

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PCT/JP2022/034357 2021-10-29 2022-09-14 制御パラメータの生成方法、プログラム、記録媒体、および、制御パラメータの生成装置 Ceased WO2023074163A1 (ja)

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CN202280071309.2A CN118140182A (zh) 2021-10-29 2022-09-14 控制参数的生成方法、程序、记录介质以及控制参数的生成装置
KR1020247017430A KR20240090974A (ko) 2021-10-29 2022-09-14 제어 파라미터의 생성 방법, 프로그램, 기록 매체, 및, 제어 파라미터의 생성 장치
US18/703,312 US20240411276A1 (en) 2021-10-29 2022-09-14 Control parameter generation method, program, recording medium, and control parameter generating device
EP22886497.1A EP4425279A4 (en) 2021-10-29 2022-09-14 CONTROL PARAMETER GENERATION METHOD, PROGRAM, RECORDING MEDIUM, AND CONTROL PARAMETER GENERATION DEVICE
JP2024139256A JP7738227B2 (ja) 2021-10-29 2024-08-20 制御パラメータの生成方法、プログラム、記録媒体、および、制御パラメータの生成装置

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WO2021053784A1 (ja) * 2019-09-19 2021-03-25 三菱電機株式会社 モータ制御装置及びモータ制御方法

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