Multi-servo drive synchronous tuning control method, device, system and storage medium

Abstract

The present invention discloses a multi-servo drive synchronous tuning control method, device, system and storage medium, comprising the following steps: a separate data thread task is allocated in a DSP kernel software framework operating system, and the task is responsible for sorting and planning the parameters fed back by each servo drive and motor. First, a concept of mathematical fuzzy control is created, and each parameter in the system is defined as several comparative parameter groups. The optimal gear of the parameter group is defined as A1, the suboptimal is defined as A2, the general excellent is defined as A3, the good is defined as B1, the sub-good is defined as B2, the general good is defined as B3, the passing is defined as C1, the sub-pass is defined as C2, the general passing is defined as C3, and the unsatisfactory is defined as D. Through a special program architecture, a PLC control core can be embedded in the operating system. Through the combination of AI big data and edge computing with fuzzy control, the EtherCat bus technology can be used to solve the problem of real-time tuning and compensation of each servo motor and drive.

Description

Technical Field

[0001] The present invention belongs to the technical field of multi-servo drives, and in particular relates to a method, device, system and storage medium for synchronous tuning control of multi-servo drives. Background Art

[0002] Servo drives, also known as "servo controllers" or "servo amplifiers", are controllers used to control servo motors. They function similarly to frequency converters acting on ordinary AC motors. They are part of the servo system and are mainly used in high-precision positioning systems. They generally control servo motors in three ways: position, speed, and torque to achieve high-precision transmission system positioning. They are high-end products in transmission technology.

[0003] Existing servo drives basically have one parameter tuning and cannot perform real-time online tuning of multiple motors at the same time. Moreover, many tuning parameters are fixed and will not be adjusted according to changes in load and motion trajectory. The motor parameters cannot be effectively adjusted. For example, the back electromotive force of the motor will change with the speed of the motor, but the traditional control method cannot track and compensate for the variables because the parameters are fixed. Summary of the invention

[0004] The technical problem to be solved by the present invention is to overcome the existing defects and provide a multi-servo drive synchronous tuning control method, device, system and storage medium to solve the problem that the existing servo drives proposed in the above background technology are basically only one parameter tuning, and multiple motors cannot be tuned online in real time at the same time, and many tuning parameters are fixed and will not be adjusted according to changes in load and motion trajectory. The parameters of the motor cannot be effectively adjusted. For example, the back electromotive force of the motor will change with the speed of the motor, but the traditional control method cannot track and compensate for the variables because the parameters are fixed.

[0005] To achieve the above object, the present invention provides the following technical solution: a multi-servo drive synchronous tuning control method, comprising the following steps:

[0006] Step 1: Assign a separate data thread task in the DSP core software architecture operating system. This task is responsible for organizing and planning the parameters fed back by each servo drive and motor. First, create a concept of mathematical fuzzy control, and define each parameter in the system into several comparative parameter groups. The best gear in the parameter group is defined as A1, the second best is defined as A2, the average is defined as A3, the good is defined as B1, the second best is defined as B2, the average good is defined as B3, the pass is defined as C1, the second best is defined as C2, the average pass is defined as C3, and the unsatisfactory is defined as D;

[0007] Step 2: Each time, the parameter group judgment within the last T period is used to tune the parameters of this parameter group, and all the data are put into the "parameter fuzzy control judgment black box" to judge the minimum and optimal parameter group. If the A1 result appears during the judgment process, it is maintained. If it is between A2 and B3 in the previous judgment results, and the output results of the project can converge the judgment results, until a satisfactory result appears;

[0008] Step 3: Design a separate real-time task in the software kernel, which can upload the server / cloud platform big data in real time through the EtherCat network bus. The AI ​​intelligent system in the cloud can perform edge computing based on the parameters in the fuzzy control black box inside the drive. Through big data analysis and iterative calculation of data streams, it helps the servo fuzzy control black box find the optimal parameter group, and then feeds the results back to the servo through the EtherCat bus network to start the synchronous gain switching and parameter adjustment mode.

[0009] Step 4: A / I artificial intelligence updates the data of the synchronous fuzzy control parameter group through the EtherCat network bus.

[0010] Preferably, in step 1, the excellence levels and rankings of the parameter groups in the framework are as follows:

[0011] A1>A2>A3>B1>B2>B3>C1>C2>C3>D, where the parameter groups include: "1. Motor back electromotive force parameters, 2. Motor output torque, 3. Motor A phase, B phase and C phase current detection value, 4. Motor feedback speed detection, 5. Motor angular velocity setting, 6. Motor plus / minus angular velocity setting, 7. Motor absolute position information, 8. Motor relative position information, 9. Servo current loop gain / integral parameters, 10. Servo speed loop gain / integral / differential parameters, 11. Servo position loop gain / integral / differential parameters, 12. Low-pass filter parameters, 13. Notch filter parameters, 14. Gain switching condition command."

[0012] Preferably, in the step 2, the satisfactory results are usually manifested in that each motor does not move / make abnormal noises, the three-loop adjustment parameter values ​​output by each driver do not overshoot, and each driver responds to the gain compensation in the link of the motor output, and can follow the target value at high speed. However, the actual operation performance of each motor should be specifically considered, because in specific applications, according to the corresponding motion control trajectory, the values ​​of the motor output are basically periodic and repetitive. Through a large number of deep learning methods, the big data results can be extracted and copied to professional storage media to facilitate the collation and collection of big data. The optimal path and parameters calculated are recorded to provide data theoretical support for AI edge computing.

[0013] Preferably, in step three, the corresponding parameter adjustment is completed by the driver, the command to be adjusted is provided by the result of A / I artificial intelligence edge computing, the judgment value after fuzzy control adjustment is the reply of the driver to A / I artificial intelligence, and A / I artificial intelligence edge computing will perform iterative calculation of each parameter in combination with the fuzzy control tuning result transmitted back each time. When the fuzzy control tuning result transmitted back is satisfied, the calculation is stopped, and the current parameter group value is regarded as the optimal result of the fixed trajectory output of this motion control.

[0014] Preferably, in step 4: the EtherCat network bus distributed clock can also be used to synchronously update multiple servo drives and motors to ensure the real-time and reliability of data update.

[0015] A multi-servo drive synchronous tuning control device, system and storage medium, including a multi-servo drive synchronous tuning control system, wherein the multi-servo drive synchronous tuning control system includes an industrial Internet of Things module, an A / I artificial intelligence edge computing module and a big data analysis module.

[0016] Preferably, the output end of the multi-servo drive synchronous tuning control system and the input end of the CPU module are electrically connected to each other.

[0017] Preferably, the CPU module and the servo driver are electrically connected to each other, and the output end of the servo driver and the input end of the motor are electrically connected to each other.

[0018] Preferably, the input end of the motor and the output end of the EtherCat bus communication module are electrically connected to each other.

[0019] Preferably, a plurality of the motors and servo drivers are provided, and a maximum of 1023 motors are provided.

[0020] Compared with the prior art, the present invention provides a method, device, system and storage medium for synchronous tuning control of multiple servo drives, which have the following beneficial effects:

[0021] 1. The present invention can embed a PLC control core in the operating system through a special program architecture, and can solve the problem of real-time tuning and compensation of each servo motor and driver through the EtherCat bus technology by combining AI big data and edge computing with fuzzy control, and effectively avoid the existing servo driver basically only has one parameter tuning, and cannot perform real-time online tuning of multiple motors at the same time, and many tuning parameters are fixed and will not be adjusted according to changes in load and motion trajectory, and the parameters of the motor cannot be effectively set. For example, the back electromotive force of the motor will change with the speed of the motor, but the traditional control method cannot track and compensate for the variables because of the fixed parameters;

[0022] 2. The present invention uses EtherCat bus technology to synchronously tune the parameters of multiple (more than 6) servo motors and drivers online in real time, and uses line fuzzy control and AI cloud edge computing algorithms and software architectures to allow multiple motors to perform perfect high-speed motion without overshoot on different motion control trajectory lines. The tuning includes: "servo current loop gain / integral tuning, servo speed loop gain / integral / differential tuning, servo position loop gain / integral / differential tuning, three-loop low-pass filter tuning," as well as real-time recording and observation of motor angular velocity, angular acceleration / deceleration, absolute position error, relative position error, and material properties and parameters of motor load output;

[0023] 3. The present invention can use EhterCat bus technology to tune synchronous drives. The present invention can synchronize up to 1024 motors, use fuzzy control technology to adjust parameters, and use edge computing technology in A / I artificial intelligence to analyze and integrate big data. It can improve the optimal path planning and optimal parameter identification of motion control, realize intelligent unattended debugging of motors, and improve the efficiency of project implementation and reduce labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0025] Figure 1 A schematic diagram of a multi-servo drive synchronous tuning control system proposed by the present invention;

[0026] Figure 2 A schematic diagram of a synchronous tuning control method for multiple servo drives proposed by the present invention;

[0027] In the figure: 1. Multi-servo drive synchronous tuning control system; 2. Industrial Internet of Things module; 3. A / I artificial intelligence edge computing module; 4. Big data analysis module; 5. CPU module; 6. Servo drive; 7. Motor; 8. EtherCat bus communication module. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] See also Figure 1-2 The present invention provides a technical solution: a multi-servo drive synchronous tuning control method, comprising the following steps:

[0030] Step 1: Assign a separate data thread task in the DSP core software architecture operating system. This task is responsible for organizing and planning the parameters fed back by each servo drive and motor. First, create a concept of mathematical fuzzy control, and define each parameter in the system into several comparative parameter groups. The best gear in the parameter group is defined as A1, the second best is defined as A2, the average is defined as A3, the good is defined as B1, the second best is defined as B2, the average good is defined as B3, the pass is defined as C1, the second best is defined as C2, the average pass is defined as C3, and the unsatisfactory is defined as D;

[0031] Step 2: Each time, the parameter group judgment within the last T period is used to tune the parameters of this parameter group, and all the data are put into the "parameter fuzzy control judgment black box" to judge the minimum and optimal parameter group. If the A1 result appears during the judgment process, it is maintained. If it is between A2 and B3 in the previous judgment results, and the output results of the project can converge the judgment results, until a satisfactory result appears;

[0032] Step 3: Design a separate real-time task in the software kernel, which can upload the server / cloud platform big data in real time through the EtherCat network bus. The AI ​​intelligent system in the cloud can perform edge computing based on the parameters in the fuzzy control black box inside the drive. Through big data analysis and iterative calculation of data streams, it helps the servo fuzzy control black box find the optimal parameter group, and then feeds the results back to the servo through the EtherCat bus network to start the synchronous gain switching and parameter adjustment mode.

[0033] Step 4: A / I artificial intelligence updates the data of the synchronous fuzzy control parameter group through the EtherCat network bus.

[0034] In the present invention, preferably, in step one, the excellent grades and rankings of the parameter groups in the framework are as follows: A1>A2>A3>B1>B2>B3>C1>C2>C3>D, wherein the parameter groups include: "1. Motor back electromotive force parameters, 2. Motor output torque, 3. Motor A phase, B phase and C phase current detection values, 4. Motor feedback speed detection, 5. Motor angular velocity setting, 6. Motor plus / minus angular velocity setting, 7. Motor absolute position information, 8. Motor relative position information, 9. Servo current loop gain / integral parameters, 10. Servo speed loop gain / integral / differential parameters, 11. Servo position loop gain / integral / differential parameters, 12. Low-pass filter parameters, 13. Notch filter parameters, 14. Gain switching condition command."

[0035] In the present invention, preferably, in step 2, the satisfactory result is usually manifested in that each motor does not move / make abnormal noise, the three-loop adjustment parameter value output by each driver does not overshoot, and each driver responds to the gain compensation slow in the link of motor output, and can follow the target value at high speed. However, the actual operation performance of each motor should be specifically considered, because in specific applications, according to the corresponding motion control trajectory, the value of the motor output is basically periodic and repetitive. Through a large number of deep learning methods, the big data results can be extracted and copied to professional storage media to facilitate the collation and collection of big data. The calculated optimal path and parameters are recorded to provide data theoretical support for AI edge computing.

[0036] In the present invention, preferably, in step three, the corresponding parameter adjustment is completed by the driver, the command to be adjusted is provided by the result of A / I artificial intelligence edge computing, the judgment value after fuzzy control adjustment is the driver's reply to A / I artificial intelligence, and A / I artificial intelligence edge computing will perform iterative calculations of various parameters in combination with the fuzzy control tuning results returned each time. When the fuzzy control tuning results returned are satisfactory, the calculation is stopped, and the current parameter group value is regarded as the optimal result of the fixed trajectory output of this motion control.

[0037] In the present invention, preferably, in step 4: the EtherCat network bus distributed clock can also be used to synchronously update multiple servo drives and motors to ensure the real-time and reliability of data update.

[0038] A multi-servo drive synchronous tuning control device, system and storage medium include a multi-servo drive synchronous tuning control system 1, and the multi-servo drive synchronous tuning control system 1 includes an industrial Internet of Things module 2, an A / I artificial intelligence edge computing module 3 and a big data analysis module 4.

[0039] In the present invention, preferably, the output end of the multi-servo drive synchronous tuning control system 1 and the input end of the CPU module 5 are electrically connected to each other.

[0040] In the present invention, preferably, the CPU module 5 and the servo driver 6 are electrically connected to each other, and the output end of the servo driver 6 and the input end of the motor 7 are electrically connected to each other.

[0041] In the present invention, preferably, the input end of the motor 7 and the output end of the EtherCat bus communication module 8 are electrically connected to each other.

[0042] In the present invention, preferably, a plurality of motors 7 and servo drivers 6 are provided, and a maximum of 1023 motors 7 are provided.

[0043] The present invention can embed a PLC control core in the operating system through a special program framework, and can solve the problem of real-time tuning and compensation of each servo motor and driver through the EtherCat bus technology by combining AI big data and edge computing with fuzzy control, and effectively avoid the problem that the existing servo drivers basically have one parameter tuning, and cannot perform real-time online tuning of multiple motors at the same time, and many tuning parameters are fixed and will not be adjusted according to changes in load and motion trajectory, and the parameters of the motor cannot be effectively adjusted. For example, the back electromotive force of the motor will change with the speed of the motor, but the traditional control method cannot track and compensate for the variables because the parameters are fixed;

[0044] Through EtherCat bus technology, multiple (more than 6) servo motors and drive parameters are tuned online in real time. The linear fuzzy control and AI cloud edge computing algorithms and software architecture are used to allow multiple motors to perform perfect high-speed motion without overshoot on different motion control trajectories. The tuning includes: "Servo current loop gain / integral tuning, servo speed loop gain / integral / differential tuning, servo position loop gain / integral / differential tuning, three-loop low-pass filter tuning," as well as real-time recording and observation of motor angular velocity, angular acceleration / deceleration, absolute position error, relative position error, and material properties and parameters of motor load output;

[0045] The present invention can utilize EhterCat bus technology to tune the synchronous driver;

[0046] The present invention can synchronously tune up to 1024 motors at most;

[0047] Use fuzzy control technology to avoid parameter adjustment;

[0048] Use edge computing technology in A / I artificial intelligence to analyze and integrate big data. Improve the optimal path planning and optimal parameter identification of motion control;

[0049] It can realize intelligent unattended debugging of motors;

[0050] It can improve the efficiency of project implementation and reduce labor costs.

[0051] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-servo drive synchronous tuning control method, characterized in that: The following steps are involved: Step 1: Assign a separate data thread task in the DSP kernel software framework operating system. This task is responsible for organizing and planning the parameters fed back by each servo drive and motor. First, create a concept of mathematical fuzzy control and define each parameter in the system into several comparative parameter groups. The excellent grades and rankings of the parameter groups in the framework are as follows: A1>A2>A3>B1>B2>B3>C1>C2>C3>D. The parameter groups include: "1. Motor back electromotive force parameters, 2. Motor output torque, 3. Motor A phase, B phase, C phase current detection value, 4. Motor feedback speed detection, 5. Motor angular velocity given , 6. Motor acceleration / deceleration angular velocity setting, 7. Motor absolute position information, 8. Motor relative position information, 9. Servo current loop gain / integral parameter, 10. Servo speed loop gain / integral / differential parameter, 11. Servo position loop gain / integral / differential parameter, 12. Low-pass filter parameter, 13. Notch filter parameter, 14. Gain switching condition command", the best gear of the parameter group is defined as A1, the second best is defined as A2, the average is defined as A3, the good is defined as B1, the second best is defined as B2, the average good is defined as B3, the pass is defined as C1, the second best is defined as C2, the average pass is defined as C3, and the unsatisfactory is defined as D; Step 2: Each time, the parameter group judgment within the last T cycle is used to tune the parameters of this parameter group, and all the data are placed in the "parameter fuzzy control judgment black box" to judge the minimum and optimal parameter group. Usually, satisfactory results are actually manifested as no abnormal movement / sound of each motor, no overshoot of the three-loop adjustment parameter values ​​output by each driver, and slow response gain compensation of each driver in the link of motor output, which can follow the target value at high speed. However, the actual operation performance of each motor should be specifically considered, because in specific applications, according to the corresponding motion control trajectory, the output value of the motor is basically periodic and repetitive. Through a large number of deep learning methods, the big data results are extracted and copied to professional storage media to facilitate the collation and collection of big data, and the calculated optimal path and parameters are recorded to provide data theoretical support for AI edge computing. If the A1 result appears in the judgment process, it is maintained. If it is between A2 and B3 in the previous judgment results, and the output results of the project can converge the judgment results until satisfactory results appear; Step 3: Design a separate real-time task in the software kernel, and upload the big data to the server / cloud platform in real time through the EtherCat network bus. The AI ​​intelligent system in the cloud performs edge computing based on the parameters in the fuzzy control black box inside the driver. Through big data analysis and iterative calculation of data streams, the servo fuzzy control black box is helped to find the optimal parameter group. The corresponding parameter adjustment is completed by the driver. The command to be adjusted is provided by the result of A / I artificial intelligence edge computing. The judgment value after fuzzy control adjustment is the driver's reply to A / I artificial intelligence. A / I artificial intelligence edge computing will perform iterative calculations on each parameter in combination with the fuzzy control tuning results transmitted back each time. When the fuzzy control tuning results transmitted back are satisfied, the calculation is stopped. The current parameter group value is regarded as the optimal result of the fixed trajectory output of this motion control. Then the result is fed back to the servo through the EtherCat bus network, and the synchronous gain switching and parameter adjustment mode are started. Step 4: A / I artificial intelligence updates the data of the synchronous fuzzy control parameter group through the EtherCat network bus.

2. The method for synchronous tuning and controlling multiple servo drives according to claim 1, characterized in that: In the step 4: using the EtherCat network bus distributed clock to synchronously update multiple servo drives and motors, to ensure the real-time and reliability of data update.

3. The control system of the multi-servo drive synchronous tuning control method according to claim 1, comprising a multi-servo drive synchronous tuning control system (1), characterized in that: The multi-servo drive synchronous tuning control system (1) comprises an industrial Internet of Things module (2), an A / I artificial intelligence edge computing module (3) and a big data analysis module (4).

4. The control system of the multi-servo drive synchronous tuning control method according to claim 3 is characterized in that: The output end of the multi-servo drive synchronous tuning control system (1) and the input end of the CPU module (5) are electrically connected to each other.

5. The control system of the multi-servo drive synchronous tuning control method according to claim 4 is characterized in that: The CPU module (5) and the servo driver (6) are electrically connected to each other, and the output end of the servo driver (6) and the input end of the motor (7) are electrically connected to each other.

6. The control system of the multi-servo drive synchronous tuning control method according to claim 5, characterized in that: The input end of the motor (7) and the output end of the EtherCat bus communication module (8) are electrically connected to each other.

7. The control system of the multi-servo drive synchronous tuning control method according to claim 1, characterized in that: A plurality of the motors (7) and servo drivers (6) are provided, and a maximum of 1023 motors (7) are provided.

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