WO2024090284A1

WO2024090284A1 - Learning system, predicting system, and adjusting system

Info

Publication number: WO2024090284A1
Application number: PCT/JP2023/037468
Authority: WO
Inventors: 貴晃新川
Original assignee: パナソニックＩｐマネジメント株式会社
Priority date: 2022-10-27
Filing date: 2023-10-17
Publication date: 2024-05-02

Abstract

A learning system (20) comprises: an acquiring unit (training data acquiring unit (27)) for acquiring an operating condition generated on the basis of an operating condition initial value, a gain condition indicating a gain parameter value of each of one or more types of gain parameter, and an operation result output from a servo amplifier (1) when the servo amplifier (1), in which the gain condition has been set, operates a motor (2) on the basis of the operating condition; and a learning model generating unit (28) which learns a relationship between the operating condition, the gain condition, and the operation result acquired by the acquiring unit (training data acquiring unit (27)), and which generates a learning model for predicting the operation result output from the servo amplifier (1) when the operating condition and the gain condition have been input and the servo amplifier (1) operates the motor (2) on the basis of the input operating condition and gain condition.

Description

Learning, predictive, and adaptive systems

The present disclosure relates to learning systems, prediction systems, and adjustment systems.

　Traditionally, there are known learning systems for adjusting parameters set in a servo amplifier. For example, Patent Document 1 discloses a positioning control device equipped with a learning unit that learns the relationship between a position command parameter and an evaluation value to obtain a learning result.

International Publication No. 2020/075316

However, the positioning control device in Patent Document 1 does not learn the relationship with the gain parameters, and there is a problem in that it cannot efficiently adjust the gain parameters set in the servo amplifier.

The present disclosure has been made to solve these problems, and aims to provide a learning system etc. that can efficiently adjust the gain parameters set in a servo amplifier.

A learning system according to one aspect of the present disclosure includes an acquisition unit that acquires operating conditions generated based on initial operating condition values, gain conditions indicating gain parameter values for each of one or more types of gain parameters, and an operating result output from a servo amplifier in which the gain conditions are set and which operates a motor based on the operating conditions, and a learning model generation unit that learns the relationship between the operating conditions, the gain conditions, and the operating result acquired by the acquisition unit, and generates a learning model that predicts the operating result output from the servo amplifier in which the servo amplifier operates the motor based on the input operating conditions and gain conditions when the operating conditions and gain conditions are input.

A prediction system according to one aspect of the present disclosure includes a learning model acquisition unit that acquires the learning model generated by the above-mentioned learning system, and an output unit that inputs the operating conditions and the gain conditions into the learning model acquired by the learning model acquisition unit and outputs a prediction result output from the learning model.

An adjustment system according to one aspect of the present disclosure includes the above-mentioned learning system and the above-mentioned prediction system.

This disclosure provides a learning system that can efficiently adjust the gain parameters set in a servo amplifier.

FIG. 1 is a block diagram showing a functional configuration of an adjustment system according to a first embodiment. FIG. 2 is a block diagram showing the functional configuration of a learning system of the adjustment system of FIG. FIG. 3 is a table showing an example of gain parameter values of the gain parameters obtained by the learning system of the adjustment system of FIG. FIG. 4 is a graph showing a first example of operating conditions generated by the learning system of the regulation system of FIG. FIG. 5 is a graph showing a second example of operating conditions generated by the learning system of the regulation system of FIG. FIG. 6 is a graph illustrating a third example of operating conditions generated by the learning system of the regulation system of FIG. FIG. 7 is a block diagram showing the functional configuration of a prediction system of the adjustment system of FIG. FIG. 8 is a flow chart showing an example of the operation of the learning system of the adjustment system of FIG. FIG. 9 is a flow chart showing an example of the operation of the prediction system of the adjustment system of FIG. FIG. 10 is a block diagram showing a functional configuration of a learning system of the adjustment system according to the second embodiment. FIG. 11 is a flow chart showing an example of the operation of the learning system of the adjustment system of FIG.

Below, embodiments of the present disclosure are described. Note that each of the embodiments described below shows a specific example of the present disclosure. Therefore, the numerical values, components, the arrangement and connection of the components, as well as the steps and the order of the steps shown in the following embodiments are merely examples and are not intended to limit the present disclosure. Therefore, among the components in the following embodiments, components that are not described in an independent claim that shows the highest concept of the present disclosure are described as optional components.

Furthermore, each figure is a schematic diagram and is not necessarily a precise illustration. In each figure, the same reference numerals are used for substantially the same configuration, and duplicate explanations are omitted or simplified.

(First embodiment)
FIG. 1 is a block diagram showing a functional configuration of an adjustment system 10 according to the first embodiment.

The adjustment system 10 is a system used to adjust gain parameters set in the servo amplifier 1. The servo amplifier 1 is a control device that controls the motor 2. For example, the motor 2 is a servo motor. In this embodiment, the motor 2 drives the conveying device 3. For example, the conveying device 3 is a device that conveys luggage, etc., and includes a one-axis reciprocating table, and also includes a modular mounter for mounting electronic components, and a feed control device for the dispenser head, etc. The adjustment system 10 may be realized by one device, or may be realized by multiple devices. For example, of the adjustment system 10, the learning data acquisition unit 27, the learning model generation unit 28, the input data acquisition unit 34, the learning model acquisition unit 35, and the prediction result output unit 36 described later may be realized by one device (such as a personal computer), and the other components may be realized by devices (such as a personal computer) different from the one device. Also, for example, the learning data acquisition unit 27, the learning model generation unit 28, the input data acquisition unit 34, the learning model acquisition unit 35, and the prediction result output unit 36 may be placed on a cloud server that communicates via the Internet, or on a server that communicates via a LAN (Local Area Network), or may be placed anywhere. Also, for example, at least a part of the adjustment system 10 may be built into the servo amplifier 1. As shown in FIG. 1, the adjustment system 10 includes a learning system 20 and a prediction system 30.

The learning system 20 is a system that performs learning. Details will be described later, but in this embodiment, the learning system 20 acquires the operation results output from the servo amplifier 1 when the servo amplifier 1 is operated based on the initial conditions, the operating conditions generated based on the initial values of the operating conditions, and the gain conditions, and learns the relationship between the initial conditions, the operating conditions, the gain conditions, and the operation results.

The prediction system 30 is a system that performs predictions. Details will be described later, but in this embodiment, the prediction system 30 uses the learning results of the learning system 20 to predict the operation results that will be output from the servo amplifier 1 when the servo amplifier 1 is operated based on the initial conditions, operating conditions, and gain conditions.

For example, a business providing the adjustment system 10, a business providing a service using the adjustment system 10, or a user using the adjustment system 10 causes the learning system 20 to learn by operating the servo amplifier 1 using the learning system 20. After the learning system 20 has learned, a user inputs gain conditions, etc. to the prediction system 30, and the prediction system 30 outputs a prediction result based on the input gain conditions, etc. If the prediction result output from the prediction system 30 is the desired result, the user can determine that the input gain conditions were appropriate. On the other hand, if the prediction result output from the prediction system 30 is not the desired result, the user can determine that the input gain conditions were not appropriate. If the prediction result output from the prediction system 30 is not the desired result, the user changes the gain conditions and inputs them to the prediction system 30, and again determines whether the prediction result output from the prediction system 30 is the desired result. The user can appropriately adjust the gain conditions by repeatedly changing the gain conditions and inputting them to the prediction system 30 until the prediction result output from the prediction system 30 is the desired result. In this way, after the learning system 20 has performed learning, the gain conditions can be adjusted without operating the servo amplifier 1 and the motor 2, etc., so the time required to adjust the gain conditions and the power consumption required to adjust the gain conditions can be reduced. By appropriately adjusting the gain conditions, the transport device 3 can operate with high responsiveness to the servo amplifier 1.

The adjustment system 10 is described in detail below.

FIG. 2 is a block diagram showing the functional configuration of the learning system 20 of the adjustment system 10 in FIG. 1.

As shown in FIG. 2, the learning system 20 has an initial condition acquisition unit 21, an operating condition initial value acquisition unit 22, a gain condition acquisition unit 23, an operating condition generation unit 24, an operating condition input unit 25, an operating result acquisition unit 26, a learning data acquisition unit 27, and a learning model generation unit 28.

The initial condition acquisition unit 21 acquires the initial conditions. For example, the initial conditions include an inertia ratio, which is the ratio between the moment of inertia of the motor 2 and the moment of inertia of the load of the conveying device 3 driven by the motor 2, and a friction compensation value for compensating for friction in the conveying device 3. For example, the adjustment system 10 accepts input of the initial conditions by a user or the like, and the initial condition acquisition unit 21 acquires the initial conditions input by the user or the like. For example, the initial conditions are accepted by input of the initial conditions via a keyboard or a touch panel, but are not particularly limited thereto.

The operating condition initial value acquisition unit 22 acquires the operating condition initial value. For example, the operating condition initial value is the initial value of the operating condition of the motor 2. For example, the operating condition initial value includes a target time for operating the motor 2, a maximum speed of the motor 2, an acceleration time of the motor 2, and a deceleration time of the motor 2. For example, the adjustment system 10 accepts input of the operating condition initial value by a user or the like, and the operating condition initial value acquisition unit 22 acquires the operating condition initial value input by the user. For example, the input of the operating condition is accepted by a keyboard or a touch panel or the like, but is not particularly limited thereto.

The gain condition acquisition unit 23 acquires the gain condition. The gain condition indicates each gain parameter value of one or more types of gain parameters. For example, the one or more types of gain parameters are multiple gain parameters, including a position gain, a speed gain, a speed integral gain, and a torque gain. That is, for example, the gain condition indicates a parameter value of a position gain, a parameter value of a speed gain, a parameter value of a speed integral gain, and a parameter value of a torque gain. For example, a table including multiple combination data (combination numbers 1 to N) is set, and the gain condition acquisition unit 23 acquires a gain condition from each of the multiple combination data. Specifically, the gain condition acquisition unit 23 automatically acquires combination numbers 1 to N one after another, and automatically acquires multiple combination data one after another. Each of the multiple combination data indicates a combination of one or more gain parameter values of one or more types of gain parameters, and the combination of one or more gain parameter values is different from each other. For example, the selection and combination of the type of one or more types of gain parameters and the one or more gain parameter values are determined based on past knowledge. For example, multiple tables may be set. For example, the adjustment system 10 accepts input of a plurality of tables by a user, a business providing the adjustment system 10, or a business providing a service by the adjustment system 10, and the gain condition acquisition unit 23 acquires gain conditions from each of the plurality of tables input by the user. Note that, for example, the user may acquire and input gain conditions from a table, and the gain condition acquisition unit 23 may acquire the gain conditions input by the user. Also, for example, the user may acquire and input a combination number from a table, and the gain condition acquisition unit 23 may acquire the gain conditions from the combination number input by the user. For example, the input of gain conditions is accepted by, but is not limited to, a keyboard or a touch panel.

FIG. 3 is a table showing an example of gain parameter values of the gain parameters acquired by the learning system 20 of the adjustment system 10 of FIG. 1. As shown in FIG. 3, for example, the table includes combination data of combination numbers 1 to N. The gain condition acquisition unit 23 acquires gain conditions from each of the combination data of combination numbers 1 to N.

Returning to FIG. 2, the operating condition generating unit 24 generates operating conditions. The operating conditions are generated based on the operating condition initial values. The operating condition generating unit 24 generates operating conditions based on the operating condition initial values acquired by the operating condition initial value acquiring unit 22.

For example, the operating condition generating unit 24 generates operating conditions determined by the operating condition initial values. As described above, for example, the operating condition initial values include a target time for operating the motor 2, a maximum speed of the motor 2, an acceleration time of the motor 2, and a deceleration time of the motor 2, and the operating condition generating unit 24 generates operating conditions determined by these. Specifically, the operating condition generating unit 24 calculates an acceleration from the maximum speed and acceleration time included in the operating condition initial values, calculates a deceleration from the maximum speed and deceleration time included in the operating condition initial values, and generates operating conditions including the acceleration and deceleration.

For example, the operating condition generating unit 24 generates control operating conditions, which are operating conditions for the servo amplifier 1 to generate an operating command for operating the motor 2, and generates learning operating conditions, which are operating conditions for the learning model generating unit 28 to learn the relationship between the operating conditions, the gain conditions, and the operating result. The operating condition generating unit 24 generates operating conditions including acceleration and deceleration as operating conditions for the learning model generating unit 28 to learn the relationship between the operating conditions, the gain conditions, and the operating result. The operating condition generating unit 24 also generates operating conditions corresponding to the operating conditions including acceleration and deceleration (learning operating conditions) as operating conditions for the servo amplifier 1 to generate an operating command for operating the motor 2. Specifically, the operating condition generating unit 24 calculates the acceleration time from the maximum speed included in the operating condition initial value and the acceleration included in the learning operating condition, calculates the deceleration time from the maximum speed included in the operating condition initial value and the deceleration included in the learning operating condition, and generates operating conditions including the target time and maximum speed included in the operating condition initial value, the acceleration time, and the deceleration time. For example, the learning operating conditions may be the same as all of the control operating conditions, or may be the same as some of the control operating conditions.

For example, the operating condition generating unit 24 generates a plurality of operating conditions by changing the operating conditions determined by the initial values of the operating conditions according to a predetermined rule. Specifically, the operating condition generating unit 24 generates operating conditions including an acceleration and deceleration obtained by changing at least one of the acceleration of the motor 2 and the deceleration of the motor 2, which are determined by the maximum speed, acceleration time, and deceleration time included in the initial values of the operating conditions, according to a predetermined rule. The details of the generation of a plurality of operating conditions by the operating condition generating unit 24 will be described later. In this way, for example, the operating condition generating unit 24 generates operating conditions for operating the motor 2 (and the conveying device 3 in this embodiment) based on the target time, maximum speed, acceleration time, and deceleration time included in the initial values of the operating conditions, and generates operating conditions for operating the motor 2 based on the acceleration and deceleration obtained by changing at least one of the acceleration of the motor 2 and the deceleration of the motor 2, which are calculated based on the maximum speed, acceleration time, and deceleration time included in the initial values of the operating conditions, according to a predetermined rule, thereby generating a plurality of operating conditions. The operating condition generating unit 24 generates multiple operating conditions for the servo amplifier 1 to generate an operating command for operating the motor 2, and the learning model generating unit 28 generates multiple operating conditions for learning the relationship between the operating conditions, gain conditions, and operating results.

FIG. 4 is a graph showing a first example of operating conditions generated by the learning system 20 of the adjustment system 10 of FIG. 1. FIG. 5 is a graph showing a second example of operating conditions generated by the learning system 20 of the adjustment system 10 of FIG. 1. FIG. 6 is a graph showing a third example of operating conditions generated by the learning system 20 of the adjustment system 10 of FIG. 1.

As shown in FIG. 4, the operating conditions here indicate the waveform of the speed of the motor 2, and the operating condition generating unit 24 generates the waveform of the speed of the motor 2.

For example, consider the case where the maximum speed included in the initial operating conditions is a = 6000 r/min, the acceleration time included in the initial operating conditions is b = 1600 msec, the deceleration time included in the initial operating conditions is c = 4200 msec, and the target time included in the initial operating conditions is y = 7800 msec. In this case, the acceleration is determined by the angle calculated by atan(a/b), and the deceleration is determined by the angle calculated by atan(a/c). Here, the angle calculated by atan(a/b) is 75° 4' 6.9" and the angle calculated by atan(a/c) is 55° 0' 28.73". For example, when rounding down to the minutes and seconds, the angle calculated by atan(a/b) is set to 75° and the angle calculated by atan(a/c) is set to 55°. For example, when rounding down to the nearest 1, the angle calculated by atan(a/b) is set to 70°, and the angle calculated by atan(a/c) is set to 50°. Furthermore, the constant speed time for operating the motor 2 at maximum speed is determined by y-a-b. Here, the constant speed time is 2000 msec. In this manner, the operating condition generating unit 24 generates operating conditions for learning that include the target time, maximum speed, acceleration time, and acceleration and deceleration times that are determined by the deceleration time included in the operating condition initial values. Furthermore, the operating condition generating unit 24 generates operating conditions for control that correspond to the operating conditions for learning.

For example, the operating condition generating unit 24 also generates operating conditions including acceleration and deceleration obtained by changing at least one of the acceleration and deceleration determined by the maximum speed, acceleration time, and deceleration time included in the initial operating condition value according to a predetermined rule. Here, the predetermined condition is a condition that the acceleration and deceleration are changed based on an angle obtained by dividing an angle by 10 within a range of 45° to 89°. Here, for example, the operating condition generating unit 24 divides the angle related to acceleration into 10 such as 45°, 50°, 55°, 60°, 65°, 70°, 75°, 80°, 85°, and 89° so as to include the above-mentioned 75°, and changes the acceleration to nine accelerations obtained based on nine angles other than 75°. For example, the acceleration obtained based on an angle of 50° is an acceleration indicated by a straight line sloping upward to the right at an angle of 50° with respect to the time axis. The operating condition generating unit 24 also divides the deceleration angle into 10, such as 45°, 50°, 55°, 60°, 65°, 70°, 75°, 80°, 85°, and 89°, so as to include the above-mentioned 55°, and changes the deceleration to 9 decelerations obtained based on 9 angles other than 55°. For example, the deceleration obtained based on an angle of 50° is the deceleration indicated by the slope of a straight line sloping downward to the right at an angle of 50° with respect to the time axis. For example, the operating condition generating unit 24 generates 100 learning operating conditions based on 10 accelerations and 10 decelerations. The operating condition generating unit 24 also generates 100 control operating conditions corresponding to the 100 learning operating conditions.

For example, the operating condition generating unit 24 may divide the angle related to acceleration into 10 parts, such as 50°, 54°, 58°, 62°, 66°, 70°, 74°, 78°, 82°, and 86°, so as to include the above-mentioned 70°. For example, the acceleration obtained based on an angle of 50° is the acceleration indicated by the slope of a straight line sloping upward to the right at an angle of 50° with respect to the time axis. The operating condition generating unit 24 may also divide the angle related to deceleration into 10 parts, such as 51°, 55°, 59°, 63°, 67°, 71°, 75°, 79°, 83°, and 87°, so as to include the above-mentioned 55°. For example, the deceleration obtained based on an angle of 51° is the deceleration indicated by the slope of a straight line sloping downward to the right at an angle of 51° with respect to the time axis.

As shown in FIG. 5, for example, the operating condition generating unit 24 generates operating conditions based on the acceleration obtained based on an angle of 50° and the deceleration obtained based on an angle of 45°.

As shown in FIG. 6, for example, if there is a limit to the distance the transport device 3 is moved, the operating condition generating unit 24 generates operating conditions by reducing the maximum speed so that the distance the transport device 3 is moved falls within the limit. Here, the operating condition generating unit 24 generates operating conditions by setting the maximum speed to 5800 r/min.

Returning to FIG. 2, the operating condition input unit 25 operates the servo amplifier 1. The operating condition input unit 25 operates the servo amplifier 1 by inputting operating conditions and the like to the servo amplifier 1. In this embodiment, the operating condition input unit 25 operates the servo amplifier 1 based on the initial conditions acquired by the initial condition acquisition unit 21, the gain conditions acquired by the gain condition acquisition unit 23, and the operating conditions generated by the operating condition generation unit 24. Specifically, the operating condition input unit 25 generates a control setting value by combining the initial condition, the gain condition, and the operating condition, and operates the servo amplifier 1 so that the motor 2 operates according to the control setting value. For example, the operating condition input unit 25 operates the servo amplifier 1 in this manner by generating a control setting value and outputting it to the servo amplifier 1. When there are multiple initial conditions, multiple gain conditions, and multiple operating conditions, the operating condition input unit 25 generates a control setting value by combining each of the multiple initial conditions, multiple gain conditions, and multiple operating conditions, and operates the servo amplifier 1. For example, when there are two types of initial conditions, four types of gain conditions, and 100 types of operating conditions, 800 control setting values are generated, and the control setting values are set one by one repeatedly 800 times to operate the servo amplifier 1. Note that, for example, the operating condition input unit 25 operates the servo amplifier 1 when no baggage is placed on the conveying device 3.

The operation result acquisition unit 26 acquires the operation result. The operation result is the operation result output from the servo amplifier 1 when the servo amplifier 1, in which the gain conditions are set, operates the motor 2 based on the operation conditions. In this embodiment, the operation result is the operation result output from the servo amplifier 1 when the servo amplifier 1, in which the gain conditions and the initial conditions are set, operates the motor 2 based on the operation conditions. For example, the operation result is the operation result of the motor 2 or the conveying device 3 when the motor 2 and the conveying device 3 are operated. For example, the servo amplifier 1 acquires the operation result of the motor 2 from a device that detects the operation of the motor 2, such as an encoder (not shown) and a camera (not shown), and outputs the acquired operation result. For example, the operation result includes the settling time of the motor 2 and the vibration level of the motor 2. For example, the settling time indicates the difference between the target time and the actual time until the motor 2 is positioned at the target position. Also, for example, the vibration level indicates the ratio between the command torque of the motor 2 and the actual torque.

The learning data acquisition unit 27 acquires learning data used for learning. The learning data acquisition unit 27 is an example of an acquisition unit that acquires operating conditions generated based on the initial values of operating conditions, gain conditions indicating the gain parameter values of one or more types of gain parameters, and an operation result output from the servo amplifier 1 when the servo amplifier 1 to which the gain conditions are set operates the motor 2 based on the operating conditions. In this embodiment, the learning data acquisition unit 27 acquires initial conditions including an inertia ratio, which is the ratio between the inertia moment of the motor 2 and the load inertia moment of the conveying device 3 driven by the motor 2, and a friction compensation value for compensating for friction in the conveying device 3, and acquires gain conditions and operation results output from the servo amplifier 1 to which the servo amplifier 1 to which the initial conditions are set operates the motor 2 based on the operating conditions. The learning data acquisition unit 27 acquires the initial conditions acquired by the initial condition acquisition unit 21, acquires the gain conditions acquired by the gain condition acquisition unit 23, acquires the learning operating conditions generated by the operating condition generation unit 24, and acquires the operation results acquired by the operation result acquisition unit 26.

The learning model generation unit 28 generates a learning model. The learning model generation unit 28 learns the relationship between the operating conditions, gain conditions, and operation results acquired by the learning data acquisition unit 27, and generates a learning model that predicts the operation result output from the servo amplifier 1 when the servo amplifier 1 operates the motor 2 based on the input operating conditions and gain conditions when the operating conditions and gain conditions are input. In this embodiment, the learning model generation unit 28 learns the relationship between the operating conditions, gain conditions, initial conditions, and operation results acquired by the learning data acquisition unit 27, and generates a learning model that predicts the operation result output from the servo amplifier 1 when the servo amplifier 1 operates the motor 2 based on the input operating conditions, gain conditions, and initial conditions when the operating conditions, gain conditions, and initial conditions are input.

For example, the learning model generation unit 28 generates a learning model that has learned the relationships between the acceleration and deceleration of the motor 2 included in the operating conditions, one or more gain parameter values included in the gain conditions, the inertia ratio and friction compensation value included in the initial conditions, and the settling time and vibration level included in the operation results. For example, the learning model generation unit 28 generates a learning model that has learned the relationships between the operating conditions, the gain conditions, the initial conditions, and the settling time, and also generates a learning model that has learned the relationships between the operating conditions, the gain conditions, the initial conditions, and the vibration level.

For example, the learning model generation unit 28 generates a learning model that has learned the tendency of change in the operation result (e.g., settling time or vibration level) with a change in the initial condition (e.g., inertia ratio and friction compensation value), the tendency of change in the operation result (e.g., settling time or vibration level) with a change in the gain condition (one or more gain parameter values), and the tendency of change in the operation result (e.g., settling time or vibration level) with a change in the operation condition (e.g., acceleration and deceleration of the motor 2).

For example, the learning model generation unit 28 generates a learning model by machine learning using a neural network. Also, for example, the learning model generation unit 28 generates a learning model by deep learning having multiple intermediate layers. In this case, the learning model generation unit 28 uses the operation results as teacher data. Also, for example, the learning model generation unit 28 generates a learning model by machine learning using regression analysis. The learning model generation unit 28 outputs the generated learning model.

In this way, the learning model generated by the learning model generation unit 28 learns the relationship between the initial conditions, operating conditions, gain conditions, and operation results, so when the initial conditions, operating conditions, and gain conditions are input, it can predict the operation results output from the servo amplifier 1 when the servo amplifier 1 operates the motor 2 based on the input initial conditions, operating conditions, and gain conditions, and outputs the predicted result (predicted operation result).

For example, the learning model generation unit 28 may train each type of operation result to generate a separate learning model, or train them all at once to generate a single learning model. For example, the learning model generation unit 28 may train the initial conditions all at once to generate a single learning model, or train them by type to generate a separate learning model. For example, when multiple initial conditions are input, the learning model generation unit 28 may train all of the initial conditions and other learning data, or train them separately. For example, the learning model generation unit 28 may train multiple pieces of learning data (initial conditions and learning operation conditions) simultaneously to generate a single learning model.

In other words, the learning model generation unit 28 may generate multiple learning models that have learned the relationships between the learning operation conditions, the gain conditions, one type of initial condition, and one type of operation result. The learning model generation unit 28 may also generate a learning model that has learned the relationships between the learning operation conditions, the gain conditions, multiple types of initial conditions, and multiple types of operation results. Specifically, for example, the learning model generation unit 28 may generate a learning model that has learned the relationships between the inertia ratio, the learning operation conditions, the gain conditions, and the settling time, and a learning model that has learned the relationships between the friction compensation, the learning operation conditions, the gain conditions, and the settling time. The learning model generation unit 28 may also generate a learning model that has learned the relationships between the inertia ratio, the friction compensation, the learning operation conditions, the gain conditions, and the settling time.

As shown in FIG. 7, the prediction system 30 has an initial condition receiving unit 31, an operating condition receiving unit 32, a gain condition receiving unit 33, an operating condition generating unit 37, an input data acquiring unit 34, a learning model acquiring unit 35, and a prediction result output unit 36.

The initial condition receiving unit 31 receives input of initial conditions. For example, the initial condition receiving unit 31 is realized by a keyboard or the like, and receives input of initial conditions by a user or the like. Note that the initial condition receiving unit 31 may also be realized by a touch panel or the like, and is not particularly limited.

The operating condition receiving unit 32 receives input of operating conditions. For example, the operating condition receiving unit 32 is realized by a keyboard or the like, and receives input of operating conditions by a user or the like. For example, the input operating conditions include a target time for operating the motor 2, a maximum speed of the motor 2, an acceleration time of the motor 2, and a deceleration time of the motor 2. Note that the operating condition receiving unit 32 may be realized by a touch panel or the like, and is not particularly limited.

The gain condition receiving unit 33 receives the input of the gain conditions. For example, the gain condition receiving unit 33 is realized by a keyboard or the like, and receives the input of the gain conditions by the user or the like. For example, the user or the like may obtain and input the gain conditions from a table, as when learning with the learning system 20, or may obtain and input the combination numbers from the table to input the gain conditions. The gain condition receiving unit 33 may also automatically obtain the combination data or combination numbers of the gain conditions from the table. The order in which the gain condition receiving unit 33 obtains the data may be from the smallest number, may be random, or may have some regularity. The gain condition receiving unit 33 may be realized by a touch panel or the like, and is not particularly limited.

The operating condition generating unit 37 generates operating conditions. For example, in the same manner as the operating condition generating unit 24, the operating condition generating unit 37 generates operating conditions determined by the target time, maximum speed, acceleration time, and deceleration time included in the operating conditions whose input has been accepted by the operating condition accepting unit 32. For example, the operating condition generating unit 37 generates operating conditions including data that matches the type of learning data included in the learning operating conditions as predictive operating conditions, which are operating conditions for prediction. For example, the predictive operating conditions generated by the operating condition generating unit 37 include the acceleration of motor 2 and the deceleration of motor 2.

The input data acquisition unit 34 acquires the input data that has been entered. In this embodiment, the input data acquisition unit 34 acquires the initial conditions accepted by the initial condition acceptance unit 31, the operating conditions generated by the operating condition generation unit 37, and the gain conditions accepted by the gain condition acceptance unit 33. In this way, the input data for prediction includes the initial conditions, operating conditions, and gain conditions, and the purpose is that the initial conditions and operating conditions are data that are uniquely determined by the user, and the gain conditions are values that the user assumes to be optimal, and the user inputs values to confirm whether the input gain conditions are optimal or not. The user repeats inputting input data until it can be confirmed that the input gain conditions are appropriate.

The learning model acquisition unit 35 acquires a learning model. The learning model acquisition unit 35 acquires a learning model generated by the learning model generation unit 28 of the learning system 20.

The prediction result output unit 36 outputs the prediction result. The prediction result output unit 36 is an example of an output unit that inputs the operating conditions and gain conditions into the learning model acquired by the learning model acquisition unit 35, and outputs the prediction result output from the learning model. In this embodiment, the prediction result output unit 36 inputs the operating conditions, gain conditions, and initial conditions acquired by the input data acquisition unit 34 into the learning model acquired by the learning model acquisition unit 35, and outputs the prediction result output from the learning model.

FIG. 8 is a block diagram showing an example of the operation of the learning system 20 of the adjustment system 10 of FIG. 1.

As shown in FIG. 8, the initial condition acquisition unit 21 acquires the initial conditions (step S1). For example, the initial condition acquisition unit 21 acquires the initial conditions as described above.

The operating condition initial value acquisition unit 22 acquires the operating condition initial value (step S2). For example, the operating condition initial value acquisition unit 22 acquires the operating condition initial value as described above.

The gain condition acquisition unit 23 acquires the gain condition (step S3). For example, the gain condition acquisition unit 23 acquires the gain condition as described above.

The operating condition generating unit 24 generates the learning operating conditions and the control operating conditions based on the operating condition initial values acquired by the operating condition initial value acquiring unit 22 (step S4). For example, the operating condition generating unit 24 generates the learning operating conditions and the control operating conditions as described above.

The operating condition input unit 25 operates the servo amplifier 1 based on the initial conditions acquired by the initial condition acquisition unit 21, the gain conditions acquired by the gain condition acquisition unit 23, and the control operating conditions generated by the operating condition generation unit 24 (step S5). For example, the operating condition input unit 25 operates the servo amplifier 1 as described above.

The operation result acquisition unit 26 acquires the operation result output from the servo amplifier 1 operated by the operation condition input unit 25 (step S6). For example, the operation result acquisition unit 26 acquires the operation result output from the servo amplifier 1 as described above.

The learning data acquisition unit 27 acquires learning data (step S7). For example, as described above, the learning data acquisition unit 27 acquires the initial conditions acquired by the initial condition acquisition unit 21, the gain conditions acquired by the gain condition acquisition unit 23, the learning operation conditions generated by the operation condition generation unit 24, and the operation results acquired by the operation result acquisition unit 26 as learning data.

The operating condition input unit 25 determines whether all operations have been completed (step S8). For example, when L initial conditions are acquired by the initial condition acquisition unit 21, N gain conditions are acquired by the gain condition acquisition unit 23, and M control operating conditions are generated by the operating condition generation unit 24, the operating condition input unit 25 determines whether operations based on each of the L×N×M combinations have been completed. If operations based on each of the L×N×M combinations have been completed, the operating condition input unit 25 determines that all operations have been completed. If operations based on each of the L×N×M combinations have not been completed, the operating condition input unit 25 determines that all operations have not been completed.

If all operations have not been completed (No in step S8), the operating condition input unit 25 operates the servo amplifier 1 again (step S5). For example, if L initial conditions are acquired by the initial condition acquisition unit 21, N gain conditions are acquired by the gain condition acquisition unit 23, and M operating conditions are generated by the operating condition generation unit 24, the operating condition input unit 25 operates the servo amplifier 1 again based on a combination of initial conditions, gain conditions, and operating conditions that have not yet been activated among the L x N x M combinations.

If the operation condition input unit 25 determines that all operations have been completed (Yes in step S8), the learning model generation unit 28 generates a learning model based on the learning data acquired by the learning data acquisition unit 27 (step S9). For example, the learning model generation unit 28 generates a learning model as described above.

Once the learning model is generated, the learning model generation unit 28 outputs the generated learning model (step S10).

FIG. 9 is a block diagram showing an example of the operation of the prediction system 30 of the adjustment system 10 of FIG. 1.

As shown in FIG. 9, the initial condition receiving unit 31 receives input of the initial conditions (step S11). For example, the initial condition receiving unit 31 receives input of the initial conditions as described above.

The operating condition receiving unit 32 receives input of the operating conditions (step S12). For example, the operating condition receiving unit 32 receives input of the operating conditions as described above.

The gain condition receiving unit 33 receives the input of the gain condition (step S13). For example, the gain condition receiving unit 33 receives the input of the gain condition as described above.

The operating condition generating unit 37 generates the predictive operating conditions (step S14). For example, the operating condition generating unit 37 generates the operating conditions as described above.

The input data acquisition unit 34 acquires input data (step S15). For example, as described above, the input data acquisition unit 34 acquires, as input data, the initial conditions accepted by the initial condition acceptance unit 31, the prediction operating conditions generated by the operating condition generation unit 37, and the gain conditions accepted by the gain condition acceptance unit 33.

The learning model acquisition unit 35 acquires the learning model generated by the learning model generation unit 28 (step S16).

The prediction result output unit 36 inputs the input data acquired by the input data acquisition unit 34 into the learning model acquired by the learning model acquisition unit 35 (step S17).

The prediction result output unit 36 inputs the input data acquired by the input data acquisition unit 34 into the learning model acquired by the learning model acquisition unit 35, and then outputs the prediction result output from the learning model (step S18).

The adjustment system 10 and other components relating to the first embodiment have been described above.

The learning system 20 according to the first embodiment includes an acquisition unit (learning data acquisition unit 27) that acquires operating conditions generated based on initial operating condition values, gain conditions indicating the respective gain parameter values of one or more types of gain parameters, and an operation result output from the servo amplifier 1 when the servo amplifier 1 to which the gain conditions are set operates the motor 2 based on the operating conditions, and a learning model generation unit 28 that learns the relationship between the operating conditions, gain conditions, and operation results acquired by the acquisition unit (learning data acquisition unit 27), and generates a learning model that predicts the operation result output from the servo amplifier 1 when the servo amplifier 1 operates the motor 2 based on the input operating conditions and gain conditions when the operating conditions and gain conditions are input.

This makes it possible to generate a learning model that predicts the operation results output from the servo amplifier 1 when the servo amplifier 1 operates the motor 2 based on the operating conditions and gain conditions, so that the operation results based on the gain conditions can be predicted without actually operating the motor 2, allowing the gain parameters to be adjusted efficiently. For example, even someone with no knowledge of how to predict optimal gain parameter values can efficiently determine the gain parameter values.

In addition, in the learning system 20 according to the first embodiment, the acquisition unit (learning data acquisition unit 27) acquires initial conditions including an inertia ratio, which is the ratio between the moment of inertia of the motor 2 and the moment of load inertia of the transport device 3 driven by the motor 2, and a friction compensation value for compensating for friction in the transport device 3, and acquires the operation results output from the servo amplifier 1 when the servo amplifier 1, in which the gain conditions and initial conditions are set, operates the motor 2 based on the operating conditions.

This makes it easier to generate a learning model that predicts the operation results output from the servo amplifier 1 when the servo amplifier 1 operates the motor 2 based on the operating conditions, gain conditions, and initial conditions, so that the operation results based on the gain conditions can be predicted without actually operating the motor 2, making it possible to adjust the gain parameters even more efficiently. The servo amplifier 1 is operated including the initial conditions, but when learning is performed using gain conditions, etc. without using the initial conditions, the operation results used as learning data become more accurate data, so a better learning model can be generated and the accuracy of the predicted operation results is improved as a result. When the servo amplifier 1 is operated including the initial conditions and used as learning data, an even better learning model can be generated and the accuracy of the predicted operation results is further improved.

In addition, in the learning system 20 according to the first embodiment, the learning model generation unit 28 learns the relationship between the operating conditions, gain conditions, initial conditions, and operating results acquired by the acquisition unit (learning data acquisition unit 27), and generates a learning model that predicts the operating results output from the servo amplifier 1 when the servo amplifier 1 operates the motor 2 based on the input operating conditions, gain conditions, and initial conditions when the operating conditions, gain conditions, and initial conditions are input.

This makes it possible to generate a learning model that predicts the operation results output from the servo amplifier 1 when the servo amplifier 1 operates the motor 2 based on the operating conditions, gain conditions, and initial conditions, so that the operation results based on the gain conditions can be predicted without actually operating the motor 2, making it possible to adjust the gain parameters even more efficiently. If the servo amplifier 1 is operated including the initial conditions and used as learning data, an even better learning model can be generated and the accuracy of the predicted operation results is further improved.

The learning system 20 according to the first embodiment also includes an operating condition generating unit 24 that generates multiple operating conditions by changing the operating conditions determined by the initial operating condition values according to a predetermined rule, and the operating condition generating unit 24 generates multiple operating conditions for the servo amplifier 1 to generate an operating command for operating the motor 2, and multiple operating conditions for the learning model generating unit 28 to learn the relationship between the operating conditions, gain conditions, and the operation results.

This allows multiple operating conditions to be generated from the initial operating condition values, and multiple operating conditions can be generated for the servo amplifier 1 to generate an operating command for operating the motor 2, and multiple operating conditions can be generated for the learning model generation unit 28 to learn the relationship between the operating conditions, gain conditions, and operation results. Therefore, the servo amplifier 1 can be efficiently operated multiple times based on multiple operating conditions, etc., and a learning model can be efficiently generated, so that the gain parameters can be adjusted even more efficiently.

In addition, in the learning system 20 according to the first embodiment, the initial operating condition values include a target time for operating the motor 2, a maximum speed of the motor 2, an acceleration time of the motor 2, and a deceleration time of the motor 2. The operating condition generating unit 24 calculates the acceleration from the maximum speed and acceleration time included in the initial operating condition values, and calculates the deceleration from the maximum speed and deceleration time included in the initial operating condition values. The learning model generating unit 28 generates operating conditions including the acceleration and deceleration as operating conditions for learning the relationship between the operating conditions, the gain conditions, and the operating results.

This allows operating conditions including acceleration and deceleration to be generated, and a learning model can be efficiently generated using operating conditions including acceleration and deceleration, allowing the gain parameters to be adjusted even more efficiently.

In addition, in the learning system 20 according to the first embodiment, the operating condition generating unit 24 generates operating conditions corresponding to operating conditions including acceleration and deceleration as operating conditions for the servo amplifier 1 to generate an operating command for operating the motor 2.

This allows the motor 2 to be operated based on operating conditions that correspond to operating conditions including acceleration and deceleration.

In addition, in the learning system 20 according to the first embodiment, the one or more types of gain parameters are multiple types of gain parameters, each of which indicates a combination of one or more gain parameter values of one or more types of gain parameters, and a gain condition acquisition unit 23 is provided that acquires gain conditions from each of multiple combination data in which the combinations of one or more gain parameter values are different from one another, and an acquisition unit (learning data acquisition unit 27) acquires the gain conditions acquired by the gain condition acquisition unit 23.

This allows multiple gain conditions to be obtained efficiently, and the servo amplifier 1 to be operated multiple times efficiently based on the multiple gain conditions, etc., and a learning model to be generated efficiently, allowing the gain parameters to be adjusted even more efficiently.

The learning system 20 according to the first embodiment also includes an operation result acquisition unit 26 that acquires the operation results output from the servo amplifier 1 when the servo amplifier 1, to which the gain conditions are set, operates the motor 2 based on the operating conditions, and an acquisition unit (learning data acquisition unit 27) acquires the operation results acquired by the operation result acquisition unit 26.

With this, the acquisition unit (learning data acquisition unit 27) can acquire the operation results output from the servo amplifier 1 via the operation result acquisition unit 26 without directly communicating with the servo amplifier 1. Therefore, a device including the acquisition unit (learning data acquisition unit 27) and the learning model generation unit 28 can be installed in a place where direct communication with the servo amplifier 1 is not possible, and the learning model can be efficiently generated, so that the gain parameters can be adjusted more efficiently. For example, the learning model generation unit 28 can be placed on a platform (such as a cloud computer) that is provided with abundant and flexible amounts of computing resources. In addition, learning data can be acquired from multiple learning systems 20 in a unified manner to generate a learning model. In addition, when learning is performed intermittently, the learning model can be continuously generated regardless of a specific learning system 20.

The prediction system 30 according to the first embodiment also includes a learning model acquisition unit 35 that acquires the learning model generated by the learning system 20, and an output unit (prediction result output unit 36) that inputs the operating conditions and gain conditions into the learning model acquired by the learning model acquisition unit 35 and outputs the prediction result output from the learning model.

This makes it possible to predict the operation results when the servo amplifier 1 operates the motor 2 based on the operating conditions and gain conditions, and output the predicted results, so that the gain parameters can be adjusted more efficiently. For example, even someone who does not have the knowledge to predict the optimal gain parameter values can efficiently determine the gain parameter values.

The prediction system 30 according to the first embodiment also includes a learning model acquisition unit 35 that acquires the learning model generated by the learning system 20, and an output unit (prediction result output unit 36) that inputs the operating conditions, gain conditions, and initial conditions into the learning model acquired by the learning model acquisition unit 35 and outputs the prediction result output from the learning model.

This allows a learning model to be generated that predicts the operational results when the servo amplifier 1 operates the motor 2 based on the operating conditions, gain conditions, and initial conditions. This makes it possible to predict the operational results based on the gain conditions without actually operating the motor 2, thereby making it possible to adjust the gain parameters even more efficiently.

The adjustment system 10 according to the first embodiment also includes the learning system 20 and the prediction system 30.

This allows the same effects and advantages to be achieved as the learning system 20 and the prediction system 30.

Second Embodiment
FIG. 10 is a block diagram showing a functional configuration of the learning system 20 of the adjustment system 10 according to the second embodiment.

As shown in FIG. 10, the learning system 20 according to the second embodiment mainly differs from the learning system 20 according to the first embodiment in that it operates multiple servo amplifiers 1. Each of the multiple servo amplifiers 1 is a control device that controls a motor 2. For example, the motor 2 is a servo motor. In this embodiment, the motor 2 drives a conveying device 3. For example, the conveying device 3 is a device that conveys luggage, etc., and includes a one-axis reciprocating table. The following will mainly explain the differences from the learning system 20 according to the first embodiment.

The operating condition input unit 25 makes A servo amplifiers 1 (A is an integer equal to or greater than 2) operate the motor 2 by making at least one of the operating conditions and the gain conditions different from each other. Specifically, the operating condition input unit 25 makes at least one of the operating conditions and the gain conditions different from each other, sets the operating conditions and the gain conditions to the A servo amplifiers 1, controls the motor 2, and operates the conveying device 3. In other words, different control setting values (combinations of operating conditions and gain conditions) are set for each of the A servo amplifiers 1. Different combinations of the N x M combinations of operating conditions and gain conditions are set in each servo amplifier 1 in sequence, and when the operation of all servo amplifiers 1 is completed and there is a combination to be set (a combination of the N x M combinations that has not yet been set), different combinations are further set in sequence for each servo amplifier 1, and this is repeated until all combinations have been set.

When one of the A servo amplifiers 1 is set as a reference servo amplifier, if the operation result output from each of the B (B is an integer of 1 or more) servo amplifiers 1 other than the reference servo amplifier among the A servo amplifiers 1 when the servo amplifier 2 is operated deviates by a predetermined amount from the operation result output from the reference servo amplifier when the reference servo amplifier operates the motor 2, the operation condition input unit 25 causes the (A-B) servo amplifiers 1, excluding the B servo amplifiers 1, to operate the motor 2 next. For example, the operation condition input unit 25 sets the initial conditions to be the same for each of the A servo amplifiers 1 (A is an integer of 2 or more), and causes the A servo amplifiers 1 to operate the motor 2 next. For example, when the settling time output from each of the B servo amplifiers 1 deviates by a predetermined amount from the settling time output from the reference servo amplifier, the operation condition input unit 25 causes the (A-B) servo amplifiers 1, excluding the B servo amplifiers 1, to operate the motor 2 next. Also, for example, when the vibration level output from each of the B servo amplifiers 1 deviates from the vibration level output from the reference servo amplifier by a predetermined amount or more, the operating condition input unit 25 causes the (A-B) servo amplifiers 1, excluding the B servo amplifiers 1 from the A servo amplifiers 1, to operate the motors 2 next. For example, the A servo amplifiers 1 operate A mutually different motors 2 to drive A mutually different conveying devices 3.

FIG. 11 is a block diagram showing an example of the operation of the learning system 20 of the adjustment system 10 of FIG. 10.

The operating condition input unit 25 operates the servo amplifier 1 based on the initial conditions acquired by the initial condition acquisition unit 21, the gain conditions acquired by the gain condition acquisition unit 23, and the operating conditions generated by the operating condition generation unit 24 (step S5). The operating condition input unit 25 operates the A servo amplifiers 1 by making at least one of the operating conditions and the gain conditions different from each other. Specifically, the operating condition input unit 25 sets different values (combinations of operating conditions and gain conditions) for each of the A servo amplifiers 1, and operates the A servo amplifiers 1.

The operation result acquisition unit 26 acquires the operation results output from the servo amplifiers 1 operated by the operation condition input unit 25 (step S6). In this embodiment, the operation result acquisition unit 26 acquires A operation results output from A servo amplifiers 1 operated by the operation condition input unit 25.

The learning data acquisition unit 27 acquires learning data (step S7). For example, as described above, the learning data acquisition unit 27 acquires the initial conditions acquired by the initial condition acquisition unit 21, the gain conditions acquired by the gain condition acquisition unit 23, the operating conditions generated by the operating condition generation unit 24, and the operating results acquired by the operating result acquisition unit 26 as learning data.

The operating condition input unit 25 judges whether all operations have been completed (step S8). For example, as described above, the operating condition input unit 25 judges whether all operations have been completed. Specifically, the operating condition input unit 25 judges that all operations have not been completed if there is a combination to be set (not yet set) among the N x M combinations of operating conditions and gain conditions, and judges that all operations have been completed if there is no combination to be set (not yet set). The operating condition input unit 25 sequentially sets the N x M combinations of operating conditions and gain conditions to the multiple servo amplifiers 1 so that they are mutually different combinations, and when the operations of all servo amplifiers 1 have been completed and there is a combination to be set, it further sequentially sets the multiple servo amplifiers 1 so that they are mutually different combinations, and repeats this until all combinations have been set.

If all operations have not been completed (No in step S8), the operating condition input unit 25 determines whether or not there is a servo amplifier 1 among the A servo amplifiers 1 that has output an operation result that deviates from the operation result output from the reference servo amplifier by a predetermined amount or more (step S21). For example, as described above, the operating condition input unit 25 determines whether or not there is a servo amplifier 1 among the A servo amplifiers 1 that has output an operation result that deviates from the operation result output from the reference servo amplifier by a predetermined amount or more.

If there is no servo amplifier 1 among the A servo amplifiers 1 that outputs an operation result that deviates from the operation result output from the reference servo amplifier by a predetermined amount or more (No in step S21), the operating condition input unit 25 operates the A servo amplifiers 1 again (step S5).

If there is a servo amplifier 1 among the A servo amplifiers 1 that outputs an operation result that deviates from the operation result output from the reference servo amplifier by a predetermined amount or more (Yes in step S21), the operating condition input unit 25 next operates (A-B) servo amplifiers 1 other than the B servo amplifiers 1 among the A servo amplifiers 1 that output an operation result that deviates from the operation result output from the reference servo amplifier by a predetermined amount or more (step S22).

If the number of generated control operating conditions is less than A, no operating conditions are set for the remaining servo amplifiers 1, the motor 2 is not controlled, and the conveying device 3 is not operated. In this case, for example, the end of the loop process can be determined not only by whether all operations have been completed, but also by whether all the generated control operating conditions have been set.

In this way, when there is one or more servo amplifiers 1 among the multiple servo amplifiers 1 that have output an operation result that deviates from the operation result output from the reference servo amplifier by a predetermined amount or more, the operating condition input unit 25 operates the servo amplifiers 1 other than the one or more servo amplifiers 1 among the multiple servo amplifiers 1 next.

The learning system 20 according to the second embodiment includes an operating condition input unit 25 that causes A (A is an integer equal to or greater than 2) servo amplifiers 1 to operate the motors 2 by making at least one of the operating conditions and the gain conditions different from each other.

According to this, A servo amplifiers 1 can be operated, the operation results can be efficiently obtained, and the learning model can be efficiently generated, so that the learning speed can be improved, a general learning model including variations can be generated, and a valid learning model that avoids the influence of inappropriate devices can be generated. For example, a learning model generated by operating multiple sets can learn including variations without being biased to the characteristics of one set of motor 2 and device, rather than a learning model generated by operating one set of servo amplifier 1, motor 2, and transport device 3. This makes it possible to generate a more general learning model. Therefore, when operating a new servo amplifier 1, motor 2, and transport device 3, it is possible to adjust the gain conditions closer to the optimum. For example, a learning model generated by operating one set of servo amplifier 1, motor 2, and transport device 3 may perform inappropriate learning due to deterioration of motor 2 and transport device 3. However, by operating multiple sets, it is possible to detect deviations in the operation results from a certain standard, so that an opportunity can be provided for a user to carry out inspections, etc., and inappropriate servo amplifiers 1, motors 2, and transport devices 3 can be eliminated. This will at least avoid generating invalid training models.

In addition, in the learning system 20 according to the second embodiment, when one of the A servo amplifiers 1 is set as a reference servo amplifier, if the operation result output from each of the B (B is an integer equal to or greater than 1) servo amplifiers 1 other than the reference servo amplifier among the A servo amplifiers 1 when the servo amplifier 1 operates the motor 2 deviates by a predetermined amount from the operation result output from the reference servo amplifier when the reference servo amplifier operates the motor 2, the operation condition input unit 25 causes the (A-B) servo amplifiers 1, excluding the B servo amplifiers 1 from the A servo amplifiers 1, to operate the motor 2 next.

According to this, B servo amplifiers 1 associated with operation results that deviate by a certain amount from the operation results when the reference servo amplifier operates the motor 2 may be faulty, etc., and the motor 2 can be operated next with (A-B) servo amplifiers 1 excluding such B servo amplifiers 1, resulting in a more normal operation result. This improves the learning speed, allows the generation of a general learning model that includes variation, and allows the generation of a valid learning model that avoids the influence of inappropriate devices.

For example, in the above explanation, operating the servo amplifier 1 means setting a control setting value in the servo amplifier 1, which generates an operation command from the control setting value to drive the motor 2 and control the conveying device 3 attached to the motor 2.

In addition, for example, the operation result output from the servo amplifier 1 means that the inertia and load of the controlled conveyor device 3 affect the drive of the motor 2, and the servo amplifier 1 acquires and processes the result of the drive of the motor 2.

Furthermore, for example, predicting the operation result output from the servo amplifier 1 means predicting the operation result that reflects the influence of the conveying device 3 without driving the motor 2 and without controlling the conveying device 3.

(Additional Note)
The above description of the embodiments and the like discloses the following techniques.

(Technique 1)
an acquisition unit that acquires operating conditions generated based on initial values of operating conditions, gain conditions indicating respective gain parameter values of one or more types of gain parameters, and an operation result output from a servo amplifier in which the gain conditions are set and which operates a motor based on the operating conditions;
a learning model generating unit that learns a relationship between the operation condition and the gain condition acquired by the acquiring unit and the operation result, and generates a learning model that predicts the operation result output from the servo amplifier when the servo amplifier operates the motor based on the input operation condition and the gain condition when the operation condition and the gain condition are input.
Learning system.

(Technique 2)
The acquisition unit is
obtain initial conditions including an inertia ratio, which is a ratio between the moment of inertia of the motor and the moment of inertia of a load of a conveying device driven by the motor, and a friction compensation value for compensating for friction in the conveying device, and obtain the operation result output from the servo amplifier in a case where the servo amplifier, in which the gain condition and the initial condition are set, operates the motor based on the operation condition;
A learning system according to technique 1.

(Technique 3)
The learning model generation unit
learning a relationship between the operation condition, the gain condition, the initial condition, and the operation result acquired by the acquisition unit, and generating the learning model that predicts the operation result output from the servo amplifier when the servo amplifier operates the motor based on the input operation condition, the gain condition, and the initial condition when the operation condition, the gain condition, and the initial condition are input;
A learning system according to technique 2.

(Technique 4)
an operating condition generating unit that generates a plurality of operating conditions by changing the operating conditions determined by the operating condition initial values in accordance with a predetermined rule;
the operating condition generating unit generates the plurality of operating conditions for generating an operating command for the servo amplifier to operate the motor, and the learning model generating unit generates the plurality of operating conditions for learning a relationship between the operating conditions, the gain condition, and the operation result;
A learning system according to any one of techniques 1 to 3.

(Technique 5)
the initial operating condition values include a target time for operating the motor, a maximum speed of the motor, an acceleration time of the motor, and a deceleration time of the motor;
the operating condition generation unit calculates an acceleration from the maximum speed and the acceleration time included in the operating condition initial value, and calculates a deceleration from the maximum speed and the deceleration time included in the operating condition initial value, and generates the operating condition including the acceleration and the deceleration as the operating condition for the learning model generation unit to learn the relationship between the operating condition, the gain condition, and the operating result.
A learning system according to technique 4.

(Technique 6)
the operating condition generating unit generates, as the operating condition for generating an operating command for the servo amplifier to operate the motor, an operating condition corresponding to the operating condition including the acceleration and the deceleration;
A learning system according to technique 5.

(Technique 7)
the one or more types of gain parameters are a plurality of types of gain parameters,
a gain condition acquisition unit that acquires the gain condition from each of a plurality of combination data, each of which indicates a combination of one or more gain parameter values of the one or more types of gain parameters and in which the combinations of the one or more gain parameter values are different from one another;
The acquisition unit is
acquiring the gain condition acquired by the gain condition acquisition unit;
A learning system according to any one of techniques 1 to 6.

(Technique 8)
an operation result acquisition unit that acquires the operation result output from the servo amplifier in which the gain condition is set and when the servo amplifier operates the motor based on the operation condition,
The acquisition unit is
acquiring the operation result acquired by the operation result acquisition unit;
A learning system according to any one of techniques 1 to 7.

(Technique 9)
an operating condition input unit that causes A number of the servo amplifiers (A is an integer equal to or greater than 2) to operate the motor by making at least one of the operating condition and the gain condition different from each other;
A learning system according to any one of techniques 1 to 8.

(Technique 10)
When one of the A servo amplifiers is set as a reference servo amplifier,
The operating condition input unit includes:
If the operation result output from each of B (B is an integer equal to or greater than 1) servo amplifiers other than the reference servo amplifier among the A servo amplifiers when the servo amplifier operates the motor deviates by a predetermined amount from the operation result output from the reference servo amplifier when the reference servo amplifier operates the motor, next operate the motor with (A-B) servo amplifiers, which are the A servo amplifiers excluding the B servo amplifiers.
A learning system according to technique 9.

(Technique 11)
A learning model acquisition unit that acquires the learning model generated by the learning system described in Technology 1;
an output unit that inputs the operation condition and the gain condition into the learning model acquired by the learning model acquisition unit, and outputs a prediction result output from the learning model;
Prediction system.

(Technique 12)
A learning model acquisition unit that acquires the learning model generated by the learning system described in Technology 3;
an output unit that inputs the operating condition, the gain condition, and the initial condition into the learning model acquired by the learning model acquisition unit, and outputs a prediction result output from the learning model;
Prediction system.

(Technique 13)
A learning system according to Technology 1;
and a prediction system according to technology 11.
Adjustment system.

(Other embodiments, etc.)
As described above, the embodiments have been described as examples of the technology disclosed in this application. However, the technology according to this disclosure is not limited to these, and can be applied to embodiments or modified examples in which changes, substitutions, additions, omissions, etc. are appropriately made without departing from the spirit of this disclosure.

Furthermore, the general or specific aspects of the present disclosure may be realized as a system, device, method, integrated circuit, computer program, or computer-readable recording medium such as a CD-ROM. Also, the present disclosure may be realized as any combination of a system, device, method, integrated circuit, computer program, and recording medium. For example, the present disclosure may be realized as a program for causing a computer device to execute the processing (method) performed by a learning system, a prediction system, or an adjustment system, or as a computer-readable non-transitory recording medium on which the program is recorded.

The learning system and the like disclosed herein can be used in systems used to adjust gain parameters set in servo amplifiers, etc.

REFERENCE SIGNS LIST 1 servo amplifier 2 motor 3 transport device 10 adjustment system 20 learning system 21 initial condition acquisition unit 22 operating condition initial value acquisition unit 23 gain

condition acquisition unit

24, 37 operating condition generation unit 25 operating condition input unit 26 operating result acquisition unit 27 learning data acquisition unit 28 learning model generation unit 30 prediction system 31 initial condition reception unit 32 operating condition reception unit 33 gain condition reception unit 34 input data acquisition unit 35 learning model acquisition unit 36 prediction result output unit

Claims

an acquisition unit that acquires operating conditions generated based on initial values of operating conditions, gain conditions indicating respective gain parameter values of one or more types of gain parameters, and an operation result output from a servo amplifier in which the gain conditions are set and which operates a motor based on the operating conditions;
a learning model generating unit that learns a relationship between the operation condition and the gain condition acquired by the acquiring unit and the operation result, and generates a learning model that predicts the operation result output from the servo amplifier when the servo amplifier operates the motor based on the input operation condition and the gain condition when the operation condition and the gain condition are input.
Learning system.
The acquisition unit is
obtain initial conditions including an inertia ratio, which is a ratio between the moment of inertia of the motor and the moment of inertia of a load of a conveying device driven by the motor, and a friction compensation value for compensating for friction in the conveying device, and obtain the operation result output from the servo amplifier in a case where the servo amplifier, in which the gain condition and the initial condition are set, operates the motor based on the operation condition;
The learning system according to claim 1 .
The learning model generation unit
learning a relationship between the operation condition, the gain condition, the initial condition, and the operation result acquired by the acquisition unit, and generating the learning model that predicts the operation result output from the servo amplifier when the servo amplifier operates the motor based on the input operation condition, the gain condition, and the initial condition when the operation condition, the gain condition, and the initial condition are input;
The learning system according to claim 2 .
an operating condition generating unit that generates a plurality of operating conditions by changing the operating conditions determined by the operating condition initial values in accordance with a predetermined rule;
the operating condition generating unit generates the plurality of operating conditions for generating an operating command for the servo amplifier to operate the motor, and the learning model generating unit generates the plurality of operating conditions for learning a relationship between the operating conditions, the gain condition, and the operation result;
A learning system according to any one of claims 1 to 3.
the initial operating condition values include a target time for operating the motor, a maximum speed of the motor, an acceleration time of the motor, and a deceleration time of the motor;
the operating condition generation unit calculates an acceleration from the maximum speed and the acceleration time included in the operating condition initial value, and calculates a deceleration from the maximum speed and the deceleration time included in the operating condition initial value, and generates the operating condition including the acceleration and the deceleration as the operating condition for the learning model generation unit to learn the relationship between the operating condition, the gain condition, and the operating result.
The learning system according to claim 4.
the operating condition generating unit generates, as the operating condition for generating an operating command for the servo amplifier to operate the motor, an operating condition corresponding to the operating condition including the acceleration and the deceleration;
The learning system according to claim 5 .
the one or more types of gain parameters are a plurality of types of gain parameters,
a gain condition acquisition unit that acquires the gain condition from each of a plurality of combination data, each of which indicates a combination of one or more gain parameter values of the one or more types of gain parameters and in which the combinations of the one or more gain parameter values are different from one another;
The acquisition unit is
acquiring the gain condition acquired by the gain condition acquisition unit;
A learning system according to any one of claims 1 to 3.
an operation result acquisition unit that acquires the operation result output from the servo amplifier in which the gain condition is set and when the servo amplifier operates the motor based on the operation condition,
The acquisition unit is
acquiring the operation result acquired by the operation result acquisition unit;
A learning system according to any one of claims 1 to 3.
an operating condition input unit that causes A number of the servo amplifiers (A is an integer equal to or greater than 2) to operate the motor by making at least one of the operating condition and the gain condition different from each other;
A learning system according to any one of claims 1 to 3.
When one of the A servo amplifiers is set as a reference servo amplifier,
The operating condition input unit includes:
If the operation result output from each of B (B is an integer equal to or greater than 1) servo amplifiers other than the reference servo amplifier among the A servo amplifiers when the servo amplifier operates the motor deviates by a predetermined amount from the operation result output from the reference servo amplifier when the reference servo amplifier operates the motor, next operate the motor with (A-B) servo amplifiers, which are the A servo amplifiers excluding the B servo amplifiers.
The learning system according to claim 9.
A learning model acquisition unit that acquires the learning model generated by the learning system according to claim 1;
an output unit that inputs the operation condition and the gain condition into the learning model acquired by the learning model acquisition unit, and outputs a prediction result output from the learning model;
Prediction system.
A learning model acquisition unit that acquires the learning model generated by the learning system according to claim 3;
an output unit that inputs the operating condition, the gain condition, and the initial condition into the learning model acquired by the learning model acquisition unit, and outputs a prediction result output from the learning model;
Prediction system.
A learning system according to claim 1;
and the prediction system according to claim 11.
Adjustment system.