Servo adjustment system

Abstract

A servo adjustment device is provided that is capable of performing automatic servo adjustment with high accuracy and in a short time, without the need for an additional device.A servo setting system 1, which sets control parameter setting information of a servo motor controlled by a control / regulation device of an industrial machine, comprises: a servo motor model 30, which is a virtualization of the operation of the servo motor; a virtual control / regulation device 20, which executes an evaluation program based on the control parameter setting information to control the servo motor model 30 virtually; and a servo setting device 10, which determines the control parameter setting information based on virtual feedback information obtained by repeatedly executing the evaluation program in the virtual control / regulation device 20 based on different control parameter setting information.

Description

Technical area

[0001] The present disclosure relates to a servo adjustment system. background

[0002] Conventionally, servo motor control parameter adjustment techniques (hereinafter referred to as servo adjustment) are known, such as gain filter adjustment, feedforward adjustment, and acceleration / deceleration adjustment. These servo adjustment techniques have been required to improve adjustment accuracy and shorten adjustment time.

[0003] However, performing servo adjustment requires feedback information about when a servo motor or machine tool, which is the control target, is actually operating. Therefore, the control target cannot be used for other purposes while performing servo adjustment. Furthermore, the time required for the actual operation of the control target and the acquisition of feedback information cannot be shortened.

[0004] On the other hand, a technique for performing servo adjustment so that the movement of a tool tip point conforms to the command has been proposed (see, for example, Patent Document 1). Furthermore, a technique for assisting servo adjustment using a virtual model has been proposed (see, for example, Patent Document 2). Citation listPatent document Patent Document 1: Unexamined Japanese Patent Application, Publication No. 2006-172149 Patent Document 2: Unexamined Japanese Patent Application, Publication No. 2017-167607 Disclosure of the invention Problems to be solved by the invention

[0005] The technology of Patent Document 1 requires an additional device such as an acceleration sensor, which increases costs. Furthermore, the technology of Patent Document 2 requires the user to specify a parameter value, which increases the workload and causes operating errors.

[0006] The present disclosure has been made in consideration of the above situation and aims to provide a servo adjustment device capable of automatically performing servo adjustment with high accuracy and in a short time without requiring additional devices. Means to solve the problems

[0007] One aspect of the present disclosure relates to a servo adjustment system for adjusting control parameter setting information of a servo motor controlled by a controller of an industrial machine, the servo adjustment system comprising: a servo motor model in which the operation of the servo motor is virtualized; a virtual controller that virtually controls the servo motor model by executing an evaluation program based on the control parameter setting information; and a servo adjustment device that determines the control parameter setting information based on virtual feedback information obtained by executing the evaluation program a plurality of times based on different pieces of the control parameter setting information in the virtual controller. Effects of the invention

[0008] According to the present disclosure, it is possible to provide a servo adjustment device capable of automatically performing servo adjustment with high accuracy and in a short time without the need for additional devices. Short description of the drawings Fig. 1 is a block diagram showing the configuration of a servo adjustment system according to a first embodiment; Fig. 2 is a flowchart showing a sequence of servo adjustment processing executed by the servo adjustment system according to the first embodiment; Fig. 3 is a flowchart showing a sequence of feedback information processing; Fig. 4 is a diagram showing information about the servo parameters; Fig. 5 is a diagram showing servo motor model information; Fig. Figure 6 is a diagram showing information about the control target model; Fig. 7 is a diagram showing command information; Fig. Figure 8 is a diagram showing the servo motor operation information determined taking into account a motor friction coefficient; Fig. 9 is a diagram showing information about the operation of the servo motor determined by considering a motor friction coefficient and a feed shaft friction coefficient; Fig. 10 is a flowchart showing a sequence of parameter setting determination processing; Fig. 11 is a diagram showing an example of parameter setting; Fig. 12 is a block diagram showing a configuration of a servo adjustment system according to a second embodiment; Fig. 13 is a block diagram showing a configuration of a servo adjustment system according to a modification of the second embodiment; Fig. 14 is a flowchart showing a sequence of servo adjustment processing executed by the servo adjustment system according to the second embodiment; Fig. 15 is a flowchart showing a sequence of preliminary determination processing of parameter setting; Fig. 16 is a diagram showing an example of a parameter setting pattern; Fig. 17 is a diagram showing an example of parameter settings applied to a plurality of virtual environments; Fig. 18 is a flowchart showing a sequence of parameter setting processing; and Fig. Figure 19 is a diagram showing an example of learning outcomes. Preferred embodiments of the invention

[0009] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. It should be noted that in the descriptions of the second and third embodiments, the same components as those of the first embodiment are denoted by the same reference numerals, and the descriptions of these components will be omitted where appropriate. (First embodiment)

[0010] A servo setting system 1 according to a first embodiment is a system that sets control parameter setting information (hereinafter referred to as parameter setting information) of a servo motor controlled by a controller of an industrial machine such as a machine tool. Fig. 1 is a block diagram showing a configuration of a servo adjustment system 1 according to a first embodiment. As shown in Fig. 1, the servo adjustment system 1 according to the first embodiment includes a servo adjustment device 10, a virtual controller 20, a servo motor model 30, and a control target model 40. The virtual controller 20, the servo motor model 30, and the control target model 40 configure a virtual environment 50.

[0011] Each of the servo adjustment devices 10 and the virtual controller 20 is a computer configured by hardware such as an arithmetic processing unit such as a CPU (Central Processing Unit), an auxiliary storage unit such as an HDD (Hard Disk Drive) and / or an SSD (Solid State Drive) for storing various computer programs, a main storage unit such as RAM (Random Access Memory) for temporarily storing data required by the arithmetic processing unit to execute a computer program, an operation unit such as a keyboard on which an operator performs various operations, and a display unit such as a display for displaying various information to the operator. The servo adjustment device 10, the virtual controller 20, and the like can transmit and receive various signals to and from each other, and the communication method therefor is not particularly limited.

[0012] The servo adjustment device 10 and / or the virtual controller 20 are communicatively connected to a numerical control device (CNC: Computerized Numerical Control) (not shown), which corresponds to a control device of an industrial machine such as a machine tool. The servo parameter information, etc., required for the servo adjustment of the present embodiment described later are part of the CNC parameters stored in the numerical control device and are acquired by the numerical control device. Furthermore, after the servo adjustment of the present embodiment, the parameter setting information is transmitted to the numerical control device and used for controlling an actual machine.

[0013] The servo adjustment device 10 performs control parameter adjustment (hereinafter referred to as servo adjustment) of the servo motor, such as gain filter adjustment, feedforward adjustment, and acceleration / deceleration adjustment. Specifically, the servo adjustment device 10 performs servo adjustment by acquiring virtual feedback information (hereinafter referred to as virtual FB information) obtained by executing an evaluation program based on various parameter adjustment information in the virtual controller 20 described later several times, and by determining parameter adjustment information based on the acquired virtual feedback information. The adjusted parameter adjustment information is transmitted to the virtual controller 20 and / or the numerical controller.

[0014] Therefore, the servo adjustment device 10 includes a virtual feedback information acquisition unit (not shown) that acquires virtual FB information transmitted from the virtual controller 20, a parameter setting determination unit (not shown) that determines parameter setting information based on a plurality of pieces of virtual FB information obtained by execution with different parameter setting information, and a parameter setting transmission unit (not shown) that transmits the determined parameter setting information to the virtual controller 20 and / or the numerical controller.

[0015] The virtual controller 20 virtually controls the servo motor model 30 by executing the evaluation program multiple times based on different parameter setting information, and generates command information, which is transmitted to the servo motor model 30 described later. Furthermore, the virtual controller 20 virtually controls a control target model 40 of a machine tool or the like, which will be described later. The virtual controller 20 transmits feedback information (hereinafter also referred to as FB information) obtained by virtually controlling the servo motor model 30 and the control target model 40 to the servo adjustment device 10. The current parameter setting information transmitted from the servo adjustment device 10 is applied to the virtual controller 20.

[0016] An evaluation program is a program created to perform servo adjustment efficiently and quickly, independent of the actual machining program, which generally requires a long machining time. Specifically, an evaluation program is a program that specifies the axial travel distance, feed rate, and the like for various machining shapes, such as a circle, a square, and a square with corners. However, an actual machining program used by the user during machining can also be used as an evaluation program instead of an evaluation program.

[0017] The servo motor model 30 is a model in which the operation and characteristics of the servo motor are virtualized. That is, the virtual environment 50 of the present embodiment includes a servo motor model 30 that virtualizes the operation of a servo motor of a machine tool or the like. More specifically, the servo motor model 30 is a model virtualized by considering the undamped natural angular frequency, the damping coefficient, the previous command time, the motor inertia, and the motor friction coefficient. As a result, an operation simulation similar to that of an actual machine becomes possible in the virtual environment 50, and virtual feedback information (hereinafter also referred to as virtual FB information) similar to the operation of an actual machine can be obtained.

[0018] The control target model 40 is a model that virtualizes the operation and characteristics of an industrial machine, such as a machine tool. That is, the virtual environment 50 of the present embodiment includes the control target model 40 that virtualizes an industrial machine, such as a machine tool. More specifically, the control target model 40 is a model virtualized by considering at least one of the spring constant, the inertia of the feed axis, the friction coefficient of the feed axis, and the disturbance torque. This enables operation simulation in the virtual environment 50 that is more similar to an actual machine, and virtual FB information that is more similar to that obtained when the actual device is operated can be obtained.

[0019] As described above, in the present embodiment, high-speed servo adjustment execution is possible because the generation of the command information for the servo motor model 30 in the virtual controller 20 and the generation of the virtual FB information in the servo motor model 30 and the control target model 40 do not require real time.

[0020] Next, a sequence of servo adjustment processing executed by the servo adjustment system 1 according to the present embodiment will be described with reference to Fig. 2 described in detail. Fig. 2 is a flowchart showing a sequence of servo adjustment processing executed by the servo adjustment system 1 according to the first embodiment. The servo adjustment processing is started, for example, in response to a user input operation on the servo adjustment device 10.

[0021] In step S11, the virtual controller 20 analyzes and executes the evaluation program. Specifically, the virtual controller 20 executes the evaluation program based on the parameter setting information (hereinafter also referred to simply as parameter setting) of the servo motor currently applied to the virtual controller 20. After that, processing proceeds to step S12. Note that this parameter setting is adjusted and changed by the servo setting processing according to the present flow.

[0022] In step S12, the virtual controller 20 generates command information for the servo motor. More specifically, in step S11 described above, the virtual controller 20 analyzes and executes the evaluation program, thereby generating command information for the servo motor. Processing then proceeds to step S13.

[0023] In step S13, the virtual controller 20 generates virtual FB information. Specifically, the virtual controller 20 generates the virtual FB information by virtually controlling and operating the servo motor model 30 and the control target model 40 based on the servo motor command information generated in step S12. Afterward, processing proceeds to step S14. The processing of the virtual FB information will be described in detail later.

[0024] In step S14, the virtual controller 20 transmits the virtual FB information generated in step S13 to the servo adjusting device 10. Thereafter, the processing proceeds to step S15.

[0025] In step S15, the servo adjustment device 10 acquires the virtual FB information transmitted from the virtual controller 20. Processing then proceeds to step S16.

[0026] In step S16, the servo adjustment device 10 determines the parameter setting based on the acquired virtual FB information. After that, processing proceeds to step S17. Note that the parameter setting determination processing will be described in detail later.

[0027] In step S17, the servo adjustment device 10 transmits the parameter setting determined in the aforementioned step S16 to the virtual control device 20. Thereafter, the processing continues with step S18.

[0028] In step S18, the virtual controller 20 acquires the parameter setting transmitted from the servo adjustment device 10. The newly acquired parameter setting is stored in the virtual controller 20 and applied to the next servo adjustment processing. Processing then continues with step S19.

[0029] In step S19, the servo adjustment device 10 determines whether the servo adjustment is completed. More specifically, the servo adjustment device 10 manages, for example, a list of adjustment parameters and determines whether the servo adjustment is completed or not based on whether the adjustment of all the parameters in the list is completed or not. Moreover, in the adjustment of each parameter, it is determined that the adjustment is not completed until the determination of the parameter adjustment based on the various virtual FB information is performed at least two or more times, that is, a plurality of times, and it is determined that the adjustment is completed when the adjustment amount of the parameter becomes, for example, 1% or less, as described later. However, whether the servo adjustment is completed or not can be determined based on a judgment by the user.If this determination is NO, the processing returns to step S11, and if it is YES, the present processing is terminated.

[0030] According to the servo setting processing described above, the parameter setting information is determined and set based on the virtual FB information obtained by analyzing and executing the evaluation program based on the various parameter setting information in the virtual controller 20 several times.

[0031] Subsequently, the generation of virtual FB information in step S13 of Fig. 2 with reference to the Fig. 3 to 6 are described in detail. Fig. 3 is a flowchart showing a sequence of virtual FB information generation processing.

[0032] In step S21, the virtual controller 20 adds servo parameter information to the virtual FB information generation element. That is, the servo parameter information is added to the virtual FB information generation element regardless of the presence or absence of the servo motor model 30 or the control target model 40. After that, processing proceeds to step S22.

[0033] Here, the virtual FB information generation element is information required for generating virtual FB information in the virtual environment 50. The servo parameter information added as the virtual FB information generation element serves as a basis for the virtual FB information generation element. This servo parameter information is included in the CNC parameters stored in a numerical controller (CNC) (not shown) communicatively connected to the servo adjustment system 1 of the present embodiment, and is transmitted from the numerical controller and stored in the virtual controller 20.

[0034] Fig. 4 is a diagram showing servo parameter information. As shown in Fig. 4, the servo parameter information includes, for example, a servo loop gain, a speed integral gain, a speed proportional gain, a phase compensation gain, a machining speed of the loop gain, a high-speed HRV (High Response Vector) current control amplifier, a shift amount of the speed integral gain, a shift amount of the speed proportional gain, a load inertia ratio, a maximum torque of the amplifier, a feedforward coefficient, and a feedforward coefficient when using an electronic gear box (EGB), a speed feedforward coefficient, a feedforward coefficient during cutting, and a speed feedforward coefficient during cutting.

[0035] Back to Fig. 3: In step S22, the virtual controller 20 determines whether or not the servo motor model 30 is present in the virtual environment 50. In the servo setting system 1 of the present embodiment, since the virtual environment 50 includes the servo motor model 30, this determination is YES, and processing proceeds to step S23. On the other hand, if a configuration is established that does not provide the servo motor model 30, the determination is NO, and processing further proceeds to step S26, where the virtual controller 20 generates virtual FB information based on the virtual FB information generation item including the servo parameter information, and then the processing is terminated.

[0036] In step S23, the virtual controller 20 adds servo motor model information to the virtual FB information generation element. The virtual FB information generation element thus contains servo parameter information and servo motor model information. Processing then proceeds to step S24.

[0037] In contrast to the servo parameter information described above, the servo motor model information here is not included in the CNC parameters, but rather is information entered by a separate file input, input via screen operation by a user, or the like, and stored in the virtual controller 20. Fig. 5 is a diagram showing the servo motor model information. As shown in Fig. 5, examples of the servo motor model information include an undamped natural angular frequency, a damping coefficient, a previous command time, a motor inertia, and a motor friction coefficient.

[0038] Back to Fig. 3: In step S24, the virtual controller 20 determines whether the control target model 40 is present in the virtual environment 50. In the servo control system 1 of the present embodiment, this determination is YES because the virtual environment 50 includes the control target model 40, and processing proceeds to step S25. On the other hand, if the control target model 40 is not provided, the determination becomes NO, and processing proceeds to step S26, where the virtual controller 20 generates virtual FB information based on the virtual FB information generation item including the servo parameter information and the servo motor model information, and then the processing ends.In this case, since the virtual FB information is generated based on the virtual FB information generation element including the servo motor model information, it is possible to generate virtual FB information more similar to a case of operating an actual machine.

[0039] In step S25, the virtual controller 20 adds the control target model information to the virtual FB information generation element. As a result, the virtual FB information generation element contains servo parameter information, servo motor model information, and control target model information.

[0040] Similar to the servo motor model information described above, the control target model information is not included in the CNC parameters and is information registered by a user through a separate file input, input through a screen operation, or the like, and stored in the virtual control device 20. Fig. Figure 6 is a diagram illustrating control target model information. As shown in Fig. 6, examples of the control target model information include a spring constant, the inertia of the feed axis, a friction coefficient of the feed axis, and a disturbance torque.

[0041] Back to Fig. 3: After the processing of step S25 is executed, the processing proceeds to step S26, where the virtual controller 20 generates virtual FB information based on the virtual FB information generation element containing the servo parameter information, the servo motor model information, and the control target model information, and then the processing is terminated. In this case, since the virtual FB information is generated based on the virtual FB information generation element containing the servo motor model information and the control target model information, it is possible to generate virtual FB information that is more similar to the virtual FB information in the case of operating an actual machine.

[0042] Regarding the above-described virtual FB information generation processing of the present embodiment, an example in which the virtual controller 20 generates the virtual FB information by calculating an error amount, which is a difference (a pulse number difference or a time difference) between the command information and the operation information of the servo motor model, will be explained with reference to FIG. Fig. 7 to 9 described in detail. Fig. 7 is a diagram showing command information. Fig. Figure 8 is a diagram showing servo motor model operating information prepared considering a motor friction coefficient. Fig. Figure 9 is a diagram showing servo motor model operating information, which was created considering a motor friction coefficient and a feed shaft friction coefficient. It should be noted that in the Fig. 8 and Fig. 9 under the hatched areas, which are different from the hatched areas of the pulses of the command information in Fig. 7, the lightly hatched area represents an area in which the number of pulses is reduced compared to the number of pulses of the command information, and the darkly hatched area represents an area in which the number of pulses is increased compared to the number of pulses of the command information.

[0043] As in Fig. 8, it is obvious that the pulse of the servo motor model operation information, which is prepared taking into account the motor friction coefficient included in the servo motor model information, is time Δt a against the impulse of the Fig. 7 shown command information is delayed. This is because, when taking into account the motor friction coefficient of the servo motor model 30, a displacement Δt aoccurs in the delay time until the servo motor model 30 actually rotates / stops with respect to the command information.

[0044] As in Fig. 9, it is also obvious that the pulse of the servo motor model operation information, which is prepared taking into account the feed shaft friction coefficient included in the control target model information in addition to the motor friction coefficient included in the servo motor model information, is delayed by a time Δt b is further delayed, which is greater than the time Δt a , from the impulse of the Fig. 7. This is because, when considering the motor friction coefficient of the servo motor model 30 and the friction coefficient of the feed shaft of the control target model 40, a deceleration Δt b the delay time occurs until the servo motor model 30 actually rotates / stops with respect to the command information.

[0045] For example, focusing on time t4, when the number of pulses of the command information is 4, the number of pulses of the servo motor model operation information created by considering the motor friction coefficient of the servo motor model 30 is 3, and thus an error amount of 1, which is the difference between the two, can be calculated. Similarly, the number of pulses of the servo motor model operation information created by considering the motor friction coefficient of the servo motor model 30 and the feed shaft friction coefficient of the control target model 40, is 2, and thus an error amount of 2, which is the difference between the servo motor model operation information and the command information, can be calculated.In this way, it is possible to calculate the error amount, which is the difference from the command information, and it is possible to generate the virtual FB information based on the calculated error amount.

[0046] Next, the parameter setting processing in step S16 of Fig. 2 with reference to Fig. 10 and Fig. 11 described in detail. Fig. 10 is a flowchart showing a sequence of parameter setting processing.

[0047] In step S31, the servo adjuster 10 selects a parameter (hereinafter referred to as the adjustment parameter) that is the target of the servo adjustment. The servo adjuster 10 stores a list of adjustment parameters in advance and automatically selects an adjustment parameter from the stored list. Alternatively, the adjustment parameters can also be selected based on user input data. After that, processing proceeds to step S32.

[0048] Here, the adjustment parameter refers to a parameter that is the target for changing the adjustment value from the parameter setting determined by acquiring the virtual FB information in the previous servo adjustment processing. In the present embodiment, only one parameter is adjusted at a time. However, the present invention is not limited to this, and a plurality of parameters can be adjusted simultaneously.

[0049] Once the setting parameter is selected, the selected state is normally maintained until the servo setting device 10 determines that the setting is completed in accordance with a predetermined rule, such as a rule to continue setting until the setting amount of the setting parameter becomes 1% or less. That is, the parameter setting determination processing is repeatedly executed, for example, until the setting amount of the setting parameter becomes 1% or less. However, it is also possible to configure the setting to be forcibly interrupted according to the user's input information and select the next setting parameter.

[0050] In step S32, the servo adjustment device 10 determines whether the change of the setting parameter selected this time is the first time. Specifically, the servo adjustment device 10 stores the number of times the parameter setting determination processing has been executed for each setting parameter, and determines, based on the stored information, whether or not the parameter setting determination processing for the setting parameter selected in step S31 is the first time. If this determination is YES, processing proceeds to step S33, and if NO, processing proceeds to step S34.

[0051] In step S33, since the currently selected setting parameter is changed for the first time, the parameter setting is determined according to a predetermined rule 1, e.g., the rule 1 for changing the setting parameter to be +10% of the initial value. After that, the present processing is terminated.

[0052] In step S34, since the change of the setting parameter selected this time is not the first time, the servo adjustment device 10 determines whether or not the current virtual FB information generated and acquired by the virtual controller 20 is a better result than the previous parameter setting. For example, in the present embodiment, if the error amount, which is the difference of the virtual FB information relative to the command information, is smaller than that at the time of the previous parameter setting, it is determined that the result is good, and conversely, if it is larger, it is determined that the result is bad. If this determination is YES, the processing proceeds to step S35, and if NO, the processing proceeds to step S36.

[0053] In step S35, since the current virtual FB information has a better result than the previous parameter setting, the parameter setting is determined according to a predetermined rule 2, e.g., rule 2, in which a value of 80% of the previous setting amount is added to the previous value in the same direction as the previous parameter setting (when the second time, since 80% is added to the +10% of the initial value, it will be +8% addition). After that, the current processing is terminated.

[0054] In step S36, since the current virtual FB information is a poor result compared to the previous parameter setting, the parameter setting is determined according to a predetermined rule 3, e.g., rule 3, in which a value of 80% of the previous setting amount is subtracted from the previous value in the direction opposite to the previous parameter setting (the second time, since 80% is subtracted from +10% of the initial value, it will be -8% subtraction). After that, the current processing is terminated.

[0055] Fig. Figure 11 is a diagram showing an example of the parameter setting described above. As shown in Fig. 11, the initial value of the selected setting parameter is, for example, 300. When the setting parameter is changed for the first time, for example, by +10% of the initial value according to Rule 1, the set value is 330. When the setting parameter is changed not for the first time but, for example, for the second time, and the current virtual FB information is a better result than that at the time of the previous parameter setting, if the value of 80% of the previous setting amount is added to the previous value to adjust in the same direction as at the time of the previous parameter setting according to Rule 2, the set value becomes 354.In addition, in a case where the change of the adjustment parameter is not the first time but, for example, the second time, and the current virtual FB information is a poor result compared with the previous parameter setting time, if the value of 80% of the previous adjustment amount is subtracted from the previous value to adjust in the direction opposite to the previous parameter setting time according to Rule 3, the adjusted value becomes 306. In this way, for example, the determination processing of the parameter setting is repeatedly performed until the adjustment amount of the adjustment parameter is 1% or less.

[0056] With the servo adjustment system 1 of the present embodiment, the following effects are achieved.

[0057] The servo adjustment system 1 according to the present embodiment is configured to include: a servo motor model 30 that virtualizes the operation of a servo motor; a virtual controller 20 that virtually controls the servo motor model 30 by executing an evaluation program based on control parameter setting information; and a servo adjustment device 10 that determines control parameter setting information based on the virtual FB information obtained from the virtual controller 20, which executes the evaluation program a plurality of times based on different control parameter setting information.

[0058] Furthermore, the servo adjustment system 1 according to the present embodiment preferably includes a control target model that virtualizes an industrial machine, and the servo adjustment device 10 is configured to acquire virtual FB information by driving the control target model 40 through the servo motor model 30 virtually controlled by the virtual controller 20.

[0059] According to the present embodiment, servo adjustment can be performed automatically based on the virtual FB information from the virtual environment 50 using the servo motor model 30, which includes the operation and characteristics of the servo motor, and the control target model 40, which includes the operation and characteristics of the industrial machine. Parameter adjustment can be automatically determined with high accuracy. Therefore, it is possible to generate virtual FB information that is more similar to the operation of an actual machine, and thus servo adjustment that is more similar to the operation of the actual machine is possible. Furthermore, compared to a case where the user determines the parameter adjustment himself, the burden on the user can be reduced and an operation error can be avoided.

[0060] Furthermore, according to the present embodiment, since servo adjustment is possible using the virtual environment 50, servo adjustment can be performed automatically in a short time by executing at high speed in the virtual environment 50. Therefore, it is possible to reduce the downtime of the equipment because no actual machine is required, and servo adjustment can be performed at the design stage.

[0061] Furthermore, according to the present embodiment, by constructing the virtual environment 50 that accurately reflects the dimensions, friction, mechanical rigidity, and the like of an industrial machine, it is possible to simulate the movement of the tool tip point from the virtual FB information, that is, from the behavior of the servo motor model, and an additional device such as an acceleration sensor is not required.

[0062] Furthermore, according to the present embodiment, since the evaluation program is executed by the virtual controller 20 to generate the virtual FB information, it is possible to easily obtain the virtual FB information for various command information by changing the evaluation program. Furthermore, an actual machining program (or a part thereof) used by the user for machining can also be used as an evaluation program, and it is not necessary to separately prepare an axis operation for evaluation, and an operation for which actual setting is desired can be easily obtained. (Second embodiment)

[0063] Fig. 12 is a block diagram showing a configuration of a servo adjustment system 1A according to a second embodiment. As shown in Fig. 12, the servo adjustment system 1A according to the second embodiment differs from the servo adjustment system 1 according to the first embodiment in the point that it includes a machine learning device 60 and a learning data storage 70, and other configurations thereof are common to the first embodiment.

[0064] Similar to the servo adjustment device 10A and the virtual controller 20, the machine learning device 60 is a computer configured by hardware such as an arithmetic processing means such as a CPU, an auxiliary storage means such as an HDD or an SSD that stores various computer programs, a main storage means such as RAM for storing data temporarily required when the arithmetic processing means executes the computer programs, an operation means such as a keyboard on which an operator performs various operations, and a display means such as a display for displaying various types of information to an operator. The machine learning device 60 and the learning data storage 70 can send and receive various signals to and from the servo adjustment device 10A and the virtual controller 20, and the communication method is not particularly limited.

[0065] The machine learning device 60 acquires virtual FB information from the virtual environment 50 via the servo adjustment device 10A and performs servo adjustment through machine learning based on the acquired virtual FB information. That is, in the first embodiment, the servo adjustment device 10 is configured to determine the control parameter setting information according to a predetermined rule based on the virtual FB information obtained based on a plurality of different parameter settings; whereas, in the present embodiment, the control parameter setting information is determined through machine learning using the machine learning device 60.

[0066] The learning data storage 70 acquires and records machine learning data including the learning results executed by this machine learning device 60. The learning results include, for example, a quality determination result according to the above-described error amount, which is a difference between the virtual FB information and the command information for the servo motor model. The learning result will be described in detail later.

[0067] The machine learning data registered in the learning data storage 70 is exchanged between the virtual environment 50 and an actual environment including a servo motor, a machine tool, a numerical control device, etc., which are not shown. This makes it possible to perform servo adjustment by machine learning more efficiently and realize servo adjustment with higher accuracy in a short time.

[0068] Here, the machine learning performed by the machine learning device 60 is not particularly limited, and supervised learning, unsupervised learning, reinforcement learning, and the like can be exemplified here. Among them, for example, reinforcement learning similar to the reinforcement learning described in Japanese Unexamined Patent Application Publication No. 2018-180764 can be preferably applied to the machine learning device 60 of the present embodiment.

[0069] More specifically, the machine learning device 60 is configured to perform reinforcement learning on parameters (e.g., parameters a i and b j (i, j ≥ 0)) of the control parameter setting information, which is, for example, the target of the servo setting. More specifically, the machine learning device 60 is configured to perform Q-learning in which the values ​​of the parameters a i and b j, the virtual FB information acquired by the virtual controller 20 executing the evaluation program, the command information for the servo motor model 30 and the like are defined as the state s, and the setting of the parameters a i and b j with respect to state s is defined as an action a. As is known to those skilled in the art, in Q-learning, the action a with the highest value Q(s, a) is selected as the optimal action from among the actions a that can be performed at a given state s. Since more detailed contents are described in Japanese Unexamined Patent Application Publication No. 2018-180764, a detailed description is omitted here.

[0070] Fig. 13 is a block diagram showing the configuration of a servo adjustment system 1B according to a modification of the second embodiment. As shown in Fig. 13, the servo adjustment system 1B according to a modification of the second embodiment differs from the servo adjustment system 1A according to the second embodiment in the point that it includes a plurality of virtual environments 51, 52, ..., 50n and a servo adjustment device 10B having a plurality of environment management units 11, and the other configurations thereof are common to the second embodiment.

[0071] Each of the plurality of virtual environments 51, 52, ... 50n has the same configuration as the virtual environment 50 of the first embodiment and the second embodiment. That is, each of the plurality of virtual environments 51, 52, ... 50n has the same configuration. Therefore, in addition to the virtual control devices 21, 22, ... 20n all having the same configuration, the servo motor models 31, 32, ... 30n all have the same configuration, and the control target models 41, 42, ... 40n all have the same configuration. Therefore, by virtually operating the model of the same machine tool and the servo motor model in a plurality of virtual environments, it is possible to acquire the virtual FB information in parallel and perform machine learning in parallel, thus enabling high-speed learning.

[0072] The multi-environment management unit 11 included in the servo adjustment device 10B manages control parameter setting information to be applied to the plurality of virtual environments 51, 52, ..., 50n. Specifically, the multi-environment management unit 11 manages parameter settings to be transmitted to the plurality of virtual environments 51, 52, ..., 50n. That is, the multi-environment management unit 11 manages which parameter or which parameter setting pattern, described later, is executed in which virtual environment.

[0073] As a result, it becomes possible to execute the evaluation program with different parameter settings in each of the virtual environments 51, 52, ... 50n, and to acquire the virtual FB information. By collecting virtual FB information from a plurality of virtual environments 51, 52, ... 50n as learning data and simultaneously performing machine learning, then registering the obtained learning result in the learning data storage 70 and sharing the learning result among the plurality of virtual environments 51, 52, ... 50n, machine learning can be performed at a higher speed.

[0074] Next, the servo adjustment processing performed by the servo adjustment system 1A according to the second embodiment will be described with reference to Fig. 14 in detail. Fig. 14 is a flowchart showing a sequence of servo adjustment processing executed by the servo adjustment system 1A according to the second embodiment. The execution of this servo adjustment processing is started, for example, in response to a user input operation on the servo adjustment device 10A. The servo adjustment processing executed by the servo adjustment system 1B according to the modification of the second embodiment is also the same as that shown in Fig. 14 shown sequence.

[0075] In step S51, the servo adjustment device 10A temporarily determines the parameter setting. After that, processing proceeds to step S52. The processing of temporarily determining the parameter setting will be described in detail later.

[0076] In step S52, the servo adjustment device 10A transmits the parameter setting provisionally determined in the aforementioned step S51 to the virtual controller 20 in the virtual environment 50. Thereafter, the processing proceeds to step S53.

[0077] In step S53, the virtual controller 20 adopts the parameter setting provisionally determined and transmitted by the servo adjustment device 10A. Processing then proceeds to step S54.

[0078] Steps S54 to S58 correspond to steps S11 to S15 of the servo adjustment processing according to the first embodiment, respectively, and the same processing is performed. That is, in the present embodiment, the virtual FB information is generated by analyzing and executing the evaluation program in the virtual controller 20 based on the parameter setting preliminarily determined in the servo adjustment device 10A. After that, the processing proceeds to step S59.

[0079] In step S59, the machine learning device 60 performs machine learning based on virtual FB information based on a plurality of different pieces of control parameter setting information transmitted and acquired from the servo setting device 10A, and generates a learning result. After that, the processing proceeds to step S60.

[0080] In step S60, the learning data storage 70 acquires the learning result obtained by machine learning from the machine learning device 60 and registers the acquired learning result in the data storage. After that, processing proceeds to step S61.

[0081] In step S61, the servo adjustment device 10A determines whether the machine learning by the machine learning device 60 is complete. If this determination is YES, the processing proceeds to step S62, and if NO, the processing returns to step S51.

[0082] In step S62, the servo adjustment device 10A determines the parameter setting. After that, processing proceeds to step S63. Note that the parameter setting determination processing will be described in detail later.

[0083] Steps S63 and S64 correspond to steps S17 and S18 of the servo adjustment processing according to the first embodiment, and the same processing is performed. After execution of step S64, the present processing is terminated.

[0084] Next, the processing of the preliminary determination of the parameter setting in step S51 described above is carried out with reference to Fig. 15 described in detail. In Fig. 15 is a flowchart showing a sequence of preliminary determination of the parameter setting.

[0085] In step S71, the servo adjustment device 10A generates the adjustment pattern of the target parameter only for the first time. Specifically, one or more parameters serving as targets for achieving an optimal value through machine learning by the machine learning device 60, that is, parameters (adjustment parameters) that are the target of servo adjustment, are determined, and a parameter adjustment pattern for executing an evaluation program is generated. After that, processing proceeds to step S72.

[0086] Fig. 16 is a diagram showing an example of a parameter setting pattern. As shown in Fig. For example, as shown in Figure 16, a two-dimensional adjustment pattern is generated by defining a minimum value, a maximum value, and a step value for each of the two parameters X and Y. Note that this adjustment pattern is automatically determined by the servo adjuster 10A. However, it can also be configured so that the user can determine the parameter adjustment pattern.

[0087] Back to Fig. 15: In step S72, the servo adjustment device 10A determines whether there are a plurality of virtual environments. In the servo adjustment system 1A according to the second embodiment, since the virtual environment is one of the virtual environments 50, and thus this determination is NO, processing proceeds to step S73. On the other hand, in the servo adjustment system 1B according to the modification of the second embodiment, since there are a plurality of virtual environments such as the virtual environments 51, 52, ..., 50n, and thus this determination is YES, processing proceeds to step S74.

[0088] In step S73, the servo setting device 10A preliminarily determines the parameter setting to be applied to the virtual environment 50 based on a case with only one virtual environment as in the second embodiment, for example, based on the parameter setting shown in Fig. 16, and the present processing is terminated.

[0089] In step S74, the multi-environment management unit 11 included in the servo adjustment device 10B preliminarily determines parameter settings to be applied to each of the plurality of virtual environments 51, 52, ..., 50n, based on, for example, the parameter setting parameter shown in Fig. 16, and the present processing is terminated.

[0090] Fig. Figure 17 is a diagram showing an example of parameter settings applied to multiple virtual environments. Fig. 17, a two-dimensional parameter setting pattern consisting of the parameters shown in Fig. 16, divided into four parts and applied to each of the virtual environments 1 to 4. Since the servo adjustment system 1B according to the modification of the second embodiment has n virtual environments, the two-dimensional parameter adjustment pattern composed of the parameters X and Y shown in Fig. 16, divided into n parts and applied to each of the virtual environments 51, 52, ... 50n. Alternatively, when the number of target parameters is large, it can be configured to acquire the virtual FB information by distributing completely different patterns in each virtual environment, such as the parameters X and Y in the virtual environment 51 and the parameters N and M in the virtual environment 52, and machine learning.

[0091] Next, the parameter setting setting process is executed in step S62 with reference to Fig. 18 described in detail. Fig. Figure 18 is a flowchart showing the sequence of the parameter setting determination process.

[0092] In step S81, the servo adjustment device 10A determines a parameter setting for which the best determination result is obtained from the learning results registered in the learning data memory 70. As a criterion for the determination, for example, a quality determination result according to an error amount, which is a difference between the virtual FB information and the command information to the servo motor model, can be specified. After that, the present processing is terminated.

[0093] Fig. Figure 19 shows a diagram showing an example of learning outcomes. As in Fig. As shown in Figure 19, the specific quality determination results include, for example, determination results such as “best”, “very good”, “good”, “pass”, and “fail” in ascending order of the error quantity for each parameter setting. Fig. 19, the best determination result is obtained for the parameter setting in which the parameter X is 160 and the parameter Y is 35, the servo setting device 10A determines this parameter setting.

[0094] It should be noted that in real-world operation, the optimal values ​​determined in the virtual environment do not necessarily fully match the real environment. Therefore, it is better, for example, to repeat the machine learning in the real environment, limiting the range determined to be "best" or "very good."

[0095] According to the servo adjustment systems 1A and 1B of the present embodiment, the following effects are exerted.

[0096] The servo adjustment system 1A according to the present embodiment further includes a machine learning device 60 that performs machine learning on the control parameter setting information using the virtual FB information, and the servo adjustment device 1A is configured to determine the control parameter setting information based on the learning result of the machine learning device 60.

[0097] Accordingly, servo adjustment can be performed using machine learning by the machine learning device 60 based on the virtual FB information obtained by the virtual control of the servo motor model 30 in the virtual environment 50. Therefore, according to the present embodiment, servo adjustment can be performed automatically with higher accuracy in a short time.

[0098] The servo adjustment system 1B according to the present embodiment is configured to include a plurality of virtual environments 51, 52, ..., 50n including the virtual controllers 21, 22, ..., 20n, the servo motor models 31, 32, ..., 30n, and the control target models 41, 42, ..., 40n. Furthermore, the servo adjustment device 10B includes the multi-environment management unit 11 that manages the control parameter setting information applied to the plurality of virtual environments 51, 52, ..., 50n, and the machine learning device 60 is configured to machine-learn the control parameter setting information using the virtual FB information obtained from the plurality of virtual environments 51, 52, ..., 50n.

[0099] As a result, the control target models 41, 42, ... 40n and the servo motor models 31, 32, ... 30n are simultaneously and virtually operated in parallel in the plurality of virtual environments 51, 52, ... 50n, so that the virtual FB information can be acquired simultaneously and in parallel. Therefore, according to the present embodiment, machine learning based on the virtual FB information can be performed at a higher speed, and further, servo adjustment can be performed automatically with high accuracy and in a short time.

[0100] It should be noted that the present disclosure is not limited to the embodiments described above, and that changes and improvements within a scope that can achieve the subject matter of the present disclosure are encompassed by the present disclosure.

[0101] In the above embodiment, a machine tool was described as an example of an industrial machine; however, the present invention is not limited thereto. The present disclosure is also applicable to other industrial machines, such as robots with servo motors.

[0102] For example, in the above embodiment, the virtual environment 50 is configured to include the control target model 40; however, it is not limited thereto. The present disclosure is also applicable to a virtual environment that does not include the control target model 40.

[0103] Furthermore, in the modification of the second embodiment, for example, each of the virtual environments 51, 52, ... 50n is configured to include the servo motor models 31, 32, ... 30n and the control target models 41, 42, ... 40n. This is not intended to be limiting. At least one of the plurality of virtual environments may be configured to include the servo motor model or the control target model, and the other virtual environments may be configured not to include the servo motor model or the control target model. Explanation of reference symbols 1, 1A, 1B servo adjustment system 10, 10A, 10B Servo adjustment device 11 Multi-environment management unit 20, 21, 22, 20n virtual control / regulation device 30, 31, 32, 30n servo motor model 40, 41, 42, 40n tax target model 50, 51, 52, 50n virtual environment 60 machine learning device 70 learning data storage QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited patent literature

[0000] JP 2006-172149

[0004] JP 2017-167607

[0004]

Claims

[1] A servo adjustment system for setting control parameter setting information of a servo motor controlled by a control device of an industrial machine, the servo adjustment system comprising: a servo motor model in which the operation of the servo motor is virtualized; a virtual controller that virtually controls the servo motor model by executing an evaluation program based on the control parameter setting information; and a servo setting device that determines the control parameter setting information based on virtual feedback information obtained by executing the evaluation program multiple times based on different pieces of the control parameter setting information in the virtual controller. [2] The servo adjustment system according to claim 1, further comprising a control target model in which the industrial machine is virtualized, wherein the servo adjustment device acquires the virtual feedback information by causing the servo motor model virtually controlled by the virtual controller to execute the control target model. [3] The servo adjustment system according to claim 1 or 2, further comprising a machine learning device that performs machine learning on the control parameter setting information using the virtual feedback information, wherein the servo adjustment device determines the control parameter setting information based on a learning result from the machine learning device. [4] A servo adjustment system according to claim 3, comprising a plurality of virtual environments each including the virtual controller and at least one of the servo motor models, wherein the servo adjustment device comprises a multi-environment management unit that manages the control parameter setting information to be applied to the plurality of virtual environments, and wherein the machine learning device performs machine learning on the control parameter setting information using the virtual feedback information obtained from the plurality of virtual environments.

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