A method for overall performance tuning of a robot manipulator servo system

Abstract

The present invention belongs to the field of intelligent robot joint servo control, and specifically relates to a method for overall performance tuning of a robot manipulator arm servo system. The manipulator arm digital twin system is used to adjust the parameters of a multi-axis manipulator arm servo system. At the same time, a grey correlation method is used to analyze which of the joint servo modules of each axis has the lowest comprehensive performance, so as to find the axis for control parameter tuning. The present invention uses the overall performance index of the manipulator arm servo system as the control parameter for performance tuning, thus avoiding the traditional method of adjusting parameters one by one, improving the overall tuning accuracy and simplifying the tuning process.

Description

Technical Field

[0001] The invention belongs to the field of intelligent robot joint servo control, and in particular relates to an overall performance tuning method for a robot mechanical arm servo system. Background Art

[0002] The robot multi-axis manipulator generally consists of 4 to 7 axes, and the most common one is a 6-axis robot with 6 degrees of freedom, which means there are 6 servo motor joints, and correspondingly, there are 6 servo drives. Before the robot leaves the factory and is installed on site, the performance of the manipulator needs to be debugged, that is, the parameters of the 6 servo systems need to be debugged to optimize its performance and meet the operational needs.

[0003] Under existing technical conditions, servo control parameters are usually debugged manually based on experience, axis by axis, until the overall performance meets the requirements. Some servo drive products have a certain function of automatically adjusting servo parameters, which can indeed reduce the debugging workload and save debugging time to a certain extent, but when the robot arm is installed under on-site working conditions, further fine-tuning is still required, and it is debugged axis by axis. There are still problems such as high experience and knowledge requirements for the debugging personnel, and time-consuming and laborious debugging.

[0004] Some servo drives also provide upper debugging software, which can show the debugging effect, such as servo position tracking, speed tracking and other curves, or system response curves, etc. The debugging personnel can judge the debugging effect according to the quality of these debugging results. This debugging method is more common in medium and high-level servo systems, providing a good tool for servo debugging and improving debugging efficiency and effect.

[0005] As the carrier of the multi-axis servo system, even if each servo system has been adjusted, the overall performance is still optimal, and it depends on whether the final execution effect meets the operation requirements. If it still cannot meet the final requirements, it is still necessary to look back and find the cause and re-adjust the parameters of a servo system.

[0006] In addition, under existing technical conditions, it is a rather troublesome task to measure the positioning accuracy of the end of the robot arm. This is reflected in the fact that other auxiliary equipment or special experimental conditions are required to test its positioning accuracy and trajectory tracking accuracy. The measurement process has high requirements for auxiliary equipment or experimental conditions, and professional personnel are required to complete the test, calculation, and finally obtain the accuracy data.

[0007] Therefore, the main problem of the prior art background is that the parameters of each servo system are adjusted one by one based on manual experience or auxiliary debugging software, which is time-consuming and labor-intensive, the means are traditional and backward, and the overall final performance cannot be controlled; and ultimately, whether the requirements are met can only be verified through experimental operation methods. Summary of the invention

[0008] In view of this, an embodiment of the present invention provides a method for tuning the overall performance of a robot manipulator servo system, and uses a manipulator digital twin system to adjust the parameters of a multi-axis manipulator servo system. At the same time, a grey correlation method is used to analyze which of the servo modules of each axis joint has the lowest comprehensive performance, so as to find the axis for control parameter tuning. The present invention uses the overall performance indicators of the manipulator servo system as control parameters for performance tuning, avoiding the traditional method of adjusting parameters one by one, improving the overall tuning progress and simplifying the tuning process.

[0009] In order to achieve the above technical objectives, the specific technical solutions adopted by the present invention are:

[0010] A method for overall performance tuning of a robot manipulator servo system, wherein the manipulator servo system comprises at least two axis joint servo modules connected in sequence; the method is characterized in that the performance tuning method comprises the following steps:

[0011] S101: Establishing a digital twin system of a robotic arm composed of digital twin models of servo modules of each joint of the robotic arm;

[0012] S102: Executing a performance feedback action of the robotic arm servo system in the digital twin system; collecting expected result data and actual result data of the digital twin system executing the performance feedback action;

[0013] S103: deriving grey correlation data based on the expected result data and the actual result data;

[0014] S104: Evaluating the comprehensive performance of each of the axis joints using a grey correlation algorithm based on the grey correlation data;

[0015] S105: Optimizing the axis joint servo module with lower comprehensive performance.

[0016] Furthermore, the tuning method also includes S106: repeating S102-S105 to further tune the robot arm servo system.

[0017] Furthermore, the grey correlation related data includes the position tracking error e of the shaft joint servo module performing the performance feedback action. θ , Position loop gain value K p , speed loop gain value K v , speed loop integral gain value K i and the speed error e w ;

[0018] The position tracking error e θ and speed error ew The expected result data is compared with the actual result data.

[0019] Furthermore, the method for evaluating the execution difference of each of the axis joint servo modules based on the grey correlation algorithm is specifically as follows:

[0020] Let the target matrix be:

[0021] X0=(x0(1),x0(2),x0(3),x0(4),x0(5))

[0022] The test results of the position tracking error of the robot arm servo system are expressed in the form of an orthogonal matrix as follows:

[0023] X i =(x i (1), x i (2), x i (3), x i (4), x i (5)), i = 1, 2, ..., n; wherein n represents the number of the axis joint servo modules;

[0024] Then the grey correlation coefficient between the test result and the target value is expressed as:

[0025]

[0026] Where γ represents X i The degree of association with X0 at point k, the mean of which is the grey association degree, is shown in the following formula:

[0027]

[0028] Among them, k = 1, 2, ..., 5; i = 1, 2, ..., n; ρ is a constant.

[0029] Furthermore, the data for building the twin model includes:

[0030] θ(t): the angle value of the axis joint servo module that changes with time in real time;

[0031] ω(t): the real-time speed value of the axis joint servo module that changes with time;

[0032] i(t): the current value of the axis joint servo module that changes with time in real time;

[0033] α(t): the real-time acceleration value of the axis joint servo module that changes with time.

[0034] Furthermore, the data for building the twin model also includes the following: visual sensor information, torque sensor information and safety sensor information of the axis joint servo module.

[0035] Furthermore, the digital twin system is also used to collect the robot arm motion equation of the performance feedback action to generate an instruction trajectory.

[0036] Furthermore, the digital twin system is provided with a graphical interface; the graphical interface is used to display the posture change trajectory of each axis joint servo module in the xyz space and the instruction trajectory.

[0037] Furthermore, the posture change trajectory and the instruction trajectory are described based on the center of mass point trajectory.

[0038] Furthermore, the process before S101 also includes an initial parameter adjustment process;

[0039] The initial parameter adjustment process comprises the following steps:

[0040] S201: Initializing and adjusting the control parameters of the robot arm servo system;

[0041] S202: initializing and adjusting the control parameters of the axis joint servo modules in the robot arm servo system in sequence from the end to the root;

[0042] S203: The robot arm servo system executes the set trajectory movement;

[0043] S204: Based on the digital twin system, compare whether the end effector of the robot arm reaches the specified target point. If yes, the initial parameter adjustment process is completed. If not, perform the following steps:

[0044] S301: Selecting the axis joint servo module with the largest tracking error based on the digital twin system for initialization tuning;

[0045] S302: Whether the tracking error of the axis joint servo module after initialization and tuning decreases, if so, jump to S203, if not, adjust the relay feedback parameter value of the axis joint servo module with the largest tracking error or manually set the axis control parameter of the axis joint servo module with the largest tracking error;

[0046] After manually setting the axis control parameters of the axis joint servo module with the largest tracking error, jump to S301.

[0047] By adopting the above technical solution, the present invention can also bring the following beneficial effects:

[0048] The present invention uses the overall performance index of the robot arm servo system as the control parameter adjustment target, and the digital twin system follows the actual robot arm servo system action in real time. It has the advantages of automatic parameter adjustment, intelligence, dynamic and intuitive adjustment process, visualization of adjustment effect, and instant presentation of result data. Especially in the digital twin model system, the tracking trajectory curve and tracking error data of each servo joint can be displayed in real time, and in theory, there is no need to actually test the tracking error of each joint. Therefore, in a sense, the work of measuring the action accuracy of the actual robot arm servo system can be omitted. The action accuracy of the robot arm servo system given by the digital twin system still has important guiding significance and practical significance in the research and development and testing stages.

[0049] The present invention can compare and analyze the errors between the positioning accuracy data of actual tests and the positioning accuracy data based on the digital twin system, so as to better correct and improve the digital twin system and output more accurate results.

[0050] The present invention brings a good experience to on-site debugging work, saves parameter debugging time and workload, and improves efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 It is a control logic diagram of the overall performance tuning method in a specific implementation manner of the present invention;

[0053] Figure 2 It is a schematic diagram of data interaction between the robot arm servo system and the digital twin system in a specific implementation manner of the present invention;

[0054] Figure 3 It is a dynamic display schematic diagram of the digital twin system in a specific implementation manner of the present invention;

[0055] Figure 4 It is a logic block diagram of the centroid trajectory calculation in a specific implementation manner of the present invention;

[0056] Figure 5 This is a schematic diagram of a digital twin system in a specific implementation manner of the present invention;

[0057] Figure 6 It is a flowchart of the initial parameter adjustment process in a specific implementation manner of the present invention. DETAILED DESCRIPTION

[0058] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0059] The following describes the embodiments of the present invention through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0060] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present invention, it should be understood by those skilled in the art that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this device and / or practice this method.

[0061] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention. The illustrations only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0062] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0063] In one embodiment of the present invention, a method for overall performance tuning of a robot manipulator servo system is provided, wherein the manipulator servo system comprises at least two axis joint servo modules connected in sequence; the performance tuning method comprises the following steps:

[0064] S101: Establishing a digital twin system of a robotic arm composed of digital twin models of servo modules of each joint of the robotic arm;

[0065] S102: Executing a performance feedback action of the robotic arm servo system in the digital twin system; collecting expected result data and actual result data of the digital twin system executing the performance feedback action;

[0066] S103: deriving grey correlation data based on the expected result data and the actual result data;

[0067] S104: Evaluating the comprehensive performance of each of the axis joints using a grey correlation algorithm based on the grey correlation data;

[0068] S105: Optimizing the axis joint servo module with lower comprehensive performance.

[0069] This embodiment establishes a digital twin system for a multi-axis robotic arm servo system, and the digital twin system can communicate and exchange data with the robotic arm servo system through a communication bus integrated in the robotic arm, such as CanOpen or EtherCat. When the robotic arm servo system is running, the operating parameters of each servo system will be uploaded to the digital twin system in real time, such as motor encoder values, current, speed, position, instructions, and corresponding time information. After receiving the above information, the digital twin system performs virtual follow-up (in some embodiments, the virtual follow-up can be displayed according to the graphical interface). At the same time, the digital twin system also runs the instruction trajectory, and can dynamically compare the operation trajectory of each axis joint servo module to follow the corresponding trajectory instruction, such as tracking error and other parameters.

[0070] When the center-of-mass point trajectory tracking error of the axis joint servo module at the end exceeds the set value, the grey correlation algorithm is used to analyze which axis joint servo module has the lowest comprehensive performance, so as to find the axis for control parameter tuning.

[0071] The joint center of mass refers to the mass center of the end-axis joint servo module (including the joint motor and reducer). In the actual end joint of the robotic arm servo system, this point is located somewhere inside the joint and is a virtual point; in the digital twin system, the corresponding point is an actual point with a specific coordinate position and movement trajectory in the xyz coordinate system. It reflects the coordinate position and movement trajectory of the center of mass point of the actual robotic arm end-axis joint servo module. In the digital twin system, when judging the trajectory of the end joint and the trajectory tracking error, the motion accuracy and performance of the robotic arm servo system control are examined based on the spatial position of the center of mass point and its movement trajectory, as well as the degree of deviation between the final trajectory point and the command trajectory.

[0072] The digital twin system of this embodiment collects the robot arm motion equation of the performance feedback action to generate a command trajectory (derived from the expected result data), and at the same time collects the data of the robot arm servo system to generate a real motion trajectory (derived from the real result data) of each axis joint servo module of the actual robot arm servo system. The trajectory and the command trajectory are dynamically run in the same coordinate system in the digital twin system at the same time, and the performance indicators such as the tracking error and dynamic response of each joint are clearly visible in real time; at the same time, the grey correlation analysis algorithm module used in the digital twin system will analyze the axis joint servo module that has the greatest impact on the terminal trajectory of the robot arm servo system, and then automatically perform parameter tuning operations on the axis joint servo module again. The obtained control parameters can be sent down and configured to the axis servo driver, so that the overall system performance of the robot arm servo system is optimized.

[0073] In some embodiments, the digital twin is provided with a graphical interface, which is used to display the position change trajectory of each axis joint servo module in the xyz space and the command trajectory. This embodiment enables the operation and comparison results of the digital twin system to be displayed in a more intuitive way, which is convenient for staff to analyze.

[0074] In one embodiment, the performance tuning method further includes S106: repeating S102-S105 to further tune the robot arm servo system. In this embodiment, the tuning method is set to a closed loop, and the axis joint character modules with the worst performance are found one by one for tuning until the deviation between the operation result of the digital twin system and the command trajectory meets the requirements.

[0075] The performance tuning method of this embodiment can be set after the initial parameter adjustment link. The robot arm servo system has a basic performance level and can meet the operating requirements of most occasions, such as the end position control accuracy requirements. However, for occasions with high performance and high precision requirements, it is necessary to fine-tune the control parameters again. This puts forward new requirements for multi-joint robot arm servo systems, such as a 6-joint robot arm servo system including Axis 1 (end effector axis joint servo module), Axis2, Axis3, Axis4, Axis5 and Axis 6 (axis joint servo module directly connected to the robot arm base), as detailed below:

[0076] This embodiment uses a grey correlation algorithm to determine which joint axis servo control parameters need to be fine-tuned.

[0077] Further, after determining the axis joint servo module to be fine-tuned, the system performs fine-tuning parameter operation (parameter self-tuning operation) on the axis to obtain better performance. There are many methods for fine-tuning the control parameters of a certain axis joint servo module. The present invention does not specify a specific method, but the relay feedback method in the embodiment of the present invention can still be used to perform a parameter self-tuning operation on a certain axis.

[0078] When the axis joint operations that require fine-tuning parameters are completed, the system re-executes a set of robot arm servo system trajectory instructions to check whether the control requirements are met. The control logic is as follows Figure 1 shown.

[0079] like Figure 1 As shown in the figure, when the system is allowed to re-fine-tune the control parameters, the robot arm servo system receives 5 sets of trajectory instructions from the digital twin system. During this process, the background database of the digital twin system automatically records the control parameters and operation data of each axis joint servo module under each set of trajectories. After the execution of 5 sets of trajectory instructions, the system executes the gray correlation algorithm module program to obtain the axis joint servo module that needs further fine-tuning. The system then performs a fine-tuning operation on the servo control parameters of the axis, obtains new control parameters, and sends them to the corresponding servo driver for update. Next, the robot arm servo system executes a set of 3 trajectory instruction actions in sequence. If all meet the performance index requirements (with the trajectory tracking error of the end joint center of mass as the main assessment indicator), the fine-tuning ends; if one does not meet the requirements, it returns and re-executes.

[0080] In the embodiment of the present invention, when the mass center trajectory of the terminal axis joint servo module cannot meet the control accuracy requirements such as positioning accuracy, it means that the servo joint performance of the robot arm servo system has not been adjusted to the optimal level, and further optimization and adjustment are necessary. But among the six joint axes, which servo joint performance needs to be adjusted? This embodiment uses the gray correlation analysis method to determine the axis.

[0081] The basic principle of grey relational analysis is Figure 1 The basic principles of "executing grey relational algorithm module" are as follows:

[0082] The center-of-mass trajectory error of the end-axis joint servo module is highly correlated with the servo performance of each joint of the manipulator servo system; assuming that each joint servo system has optimal performance, such as high bandwidth, low overshoot, etc., without considering other influencing factors, such as trajectory instruction transmission speed, external disturbance influence, etc., the overall operation of the manipulator servo system should have a high response bandwidth and high position control accuracy of each axis, so as to ensure that the center-of-mass trajectory tracking error of the end-axis joint servo module is small and achieve the purpose of high-precision tracking trajectory instructions. However, when the center-of-mass trajectory tracking error of the end-axis joint servo module is large, and the servo performance of each axis joint meets the requirements, it is still necessary to find the axis that has the greatest impact on the center-of-mass tracking trajectory error of the end-axis joint servo module and optimize the control parameters to achieve better and more optimal performance, so as to ensure that the center-of-mass trajectory tracking error of the end-axis joint servo module is within the required range.

[0083] In one embodiment, the grey correlation related data includes: position tracking error e of the shaft joint servo module performing the performance feedback action; θ , Position loop gain value K p , speed loop gain value K v , speed loop integral gain value K i and the speed error e w .

[0084] The position tracking error e θ and speed error e w Based on the comparison between the expected result data and the actual result data, these five data are used as evaluation source data to conduct a comprehensive performance evaluation on each axis joint servo module.

[0085] In this embodiment, the method for evaluating the execution difference of each of the axis joint servo modules based on the grey correlation algorithm is specifically as follows:

[0086] Let the target matrix be:

[0087] X0=(x0(1),x0(2),x0(3),x0(4),x0(5))

[0088] The test results of the position tracking error of the robot arm servo system are expressed in the form of an orthogonal matrix as follows:

[0089] X i =(x i (1), x i (2), x i (3), x i (4), x i (5)), i = 1, 2, ..., n; wherein n represents the number of the axis joint servo modules;

[0090] Then the grey correlation coefficient between the test result and the target value is expressed as:

[0091]

[0092] Where γ represents X i The degree of association with X0 at point k, the mean of which is the grey association degree, is shown in the following formula:

[0093]

[0094] Among them, k = 1, 2, ..., 5; i = 1, 2, ..., n; ρ is a constant.

[0095] The ρ is taken as 0.5.

[0096] This embodiment takes 6 groups of axis joint servo modules as an example, and the data matrix table is shown in the following table:

[0097] Table 1 Test data (example)

[0098]

[0099] After standardizing and finding the maximum value of the data in the table, the system calculates the grey correlation degree of the six axes respectively: ξ(X0, X i ); The highest value indicates that the axis has the best servo comprehensive performance, and the lowest value indicates that the axis has the lowest servo performance. The system will automatically re-optimize the control parameters of the lowest performance axis joint servo module.

[0100] In one embodiment, the data for building the twin model includes:

[0101] θ(t): the angle value of the axis joint servo module that changes with time in real time;

[0102] ω(t): the real-time speed value of the axis joint servo module that changes with time;

[0103] i(t): the current value of the axis joint servo module that changes with time in real time;

[0104] α(t): the real-time acceleration value of the axis joint servo module that changes with time;

[0105] The relevant operating parameters of the servo modules of each axis joint are uploaded to the digital twin system in real time, driving the corresponding twin model in the digital twin system to perform the same action. Figure 2 shown.

[0106] In this embodiment, the data for building the twin model also includes the following: visual sensor information, torque sensor information, and safety sensor information of the axis joint servo module.

[0107] The joint servo driver transmits the joint motor angle, speed, acceleration and current value to the corresponding twin model in real time to drive the twin model to move. In addition, the sensor data such as the visual sensor, torque sensor, safety sensor installed on the robot arm servo system is also transmitted to the digital twin in real time as the control input of the perception system model of the digital twin system.

[0108] In one embodiment, when the robotic arm servo drive system completes the tuning parameter adjustment or the initial parameter adjustment, the latest control parameters obtained can be sent to the corresponding robotic arm servo system actual servo driver through the digital twin system, thereby completing the task of updating the control parameters.

[0109] In one implementation, the digital twin system is also used to collect the robot arm motion equation of the performance feedback action to generate a command trajectory. The digital twin system is provided with a graphical interface; the graphical interface is used to display the posture change trajectory of each axis joint servo module in the xyz space and the command trajectory. The posture change trajectory and the command trajectory are described based on the center of mass point trajectory.

[0110] In this embodiment, the motion state of the servo modules of each axis joint of the robot arm servo system and the trajectory of the posture change in the xyz space can be viewed on the graphical interface. After comparing with the command trajectory, the size of the trajectory tracking error can be intuitively seen or displayed as a data curve. It is also possible to track, display and analyze a certain parameter separately to provide assistance for parameter adjustment work, such as Figure 3 shown.

[0111] Figure 3 In the figure, the xy plane is the horizontal plane; the xz plane is the vertical plane, and the origin of the xyz coordinates is at the origin of the base of the robot servo system.

[0112] For the sake of simplicity, let the robot servo system perform a certain trajectory movement only in the xz plane, and let the end effector rise by an angle. Then the digital twin system calculates the initial position of the center of mass of the end axis joint servo module and its walking trajectory curve based on known data such as the physical dimensions of the robot servo system. When the robot servo system completes the command trajectory, the system interface will display the command trajectory, actual running trajectory and final trajectory point position of the center of mass point, and display the trajectory error data of the final stop point.

[0113] In addition, the graphical interface can also be used to display and analyze the trajectory and error curve of a single axis joint servo module.

[0114] In this embodiment, taking the robot arm servo system including 6 axis joint servo modules as an example, at the beginning of the calculation of the center of mass trajectory, it is necessary to collect the initial states of the 6 axes, that is, the feedback angle values ​​θ of the encoders of the respective joint motors. x (k0); x is 1 to 6, indicating 6 axes; the initial positioning data D of the visual sensor must also be collected xyz (k0); In order to improve the accuracy of the visual sensor positioning data, it is necessary to set a special fixed reference object on the platform or environment where the robotic arm servo system is located. In this embodiment, a QR code can be affixed to the platform where the robotic arm servo system is located. The visual sensor of the robotic arm servo system can determine the spatial position of the current end joint by shooting the QR code; and, when the robotic arm servo system ends the action, the final spatial position of the end of the robotic arm servo system is also determined by shooting the QR code, thereby assisting the center of mass trajectory calculation module in the digital twin system to improve the calculation accuracy.

[0115] It should be pointed out that the QR code is not necessary and can be replaced by other physical objects with fixed positions in the environment. It is also important to point out that the visual sensor data is not necessary for calculating the trajectory of the center of mass point, but is only used to assist in improving its calculation accuracy. In theory, after knowing the initial posture data of the robot servo system, the spatial position data of the center of mass point can be obtained based only on the real-time angle of each joint motor encoder, the motion equation of the robot servo system, and the physical size data of the robot servo system.

[0116] like Figure 4 As shown in the figure, during the operation of the robotic arm servo system, the digital twin system collects the angle information of the motor encoders of each joint and the position coordinate information of the visual sensor in real time, and compares them with the previous values ​​to generate the difference, thereby calculating the current spatial position of the center of mass, and finally generating the center of mass spatial trajectory point data.

[0117] In one embodiment, the process before S101 also includes an initial parameter adjustment process;

[0118] The initial parameter adjustment process comprises the following steps:

[0119] S201: Initializing and adjusting the control parameters of the robot arm servo system;

[0120] S202: initializing and adjusting the control parameters of the axis joint servo modules in the robot arm servo system in sequence from the end to the root;

[0121] S203: The robot arm servo system executes the set trajectory movement;

[0122] S204: Based on the digital twin system, compare whether the end effector of the robot arm reaches the specified target point. If yes, the initial parameter adjustment process is completed. If not, perform the following steps:

[0123] S301: Selecting the axis joint servo module with the largest tracking error based on the digital twin system for initialization tuning;

[0124] S302: Whether the tracking error of the axis joint servo module after initialization and tuning decreases, if so, jump to S203, if not, adjust the relay feedback parameter value of the axis joint servo module with the largest tracking error or manually set the axis control parameter of the axis joint servo module with the largest tracking error;

[0125] After manually setting the axis control parameters of the axis joint servo module with the largest tracking error, jump to S301.

[0126] like Figure 5 As shown, the digital twin system is installed on a computer, and a communication cable is connected between the computer and the robotic arm servo system.

[0127] When performing initial parameter adjustment, first execute Axis 1 (the end effector axis joint servo module), then execute Axis 2, Axis 3, Axis 4, Axis 5 in sequence, and finally Axis 6 (the axis joint servo module directly connected to the robot arm base).

[0128] In one embodiment, the parameter self-tuning method adopted by the system is the relay feedback method. During the execution, each joint servo axis does not perform any visible motion trajectory, and only presents a micro-vibration state lasting about 1 second to complete the parameter adjustment and initialization of one axis. Therefore, it takes about 6 seconds to complete the execution of 6 axes.

[0129] After the initial parameters of each axis are set, the digital twin system will automatically send a set of joint action instructions to the robot arm servo system, and the actual robot arm servo system will perform the corresponding actions according to the action instructions. The actual joint actions are displayed in real time on the digital twin system interface. If the position control tracking error of the servo module of each axis joint is within the specified setting value, the parameter initialization operation is completed. If the tracking error of an axis is higher than the setting value, it is necessary to re-perform the parameter adjustment of the joint separately, or manually set it until the requirements are met.

[0130] The logic diagram of the overall adjustment process is as follows: Figure 6 shown.

[0131] Figure 6 middle:

[0132] Err_axisX: indicates the position tracking error of the X axis, X is one of 1, 2, 3, 4, 5, and 6;

[0133] Err0_axisX: indicates the position tracking error after re-performing the X-axis parameter adjustment;

[0134] Max(Err_axisX): indicates the tracking error of the axis with the largest tracking error among Axis 1, Axis 2, Axis 3, Axis 4, Axis 5 and Axis 6;

[0135] As mentioned above, the initial parameter adjustment is performed from Axis 1 to Axis 6 in sequence. After one execution, the system determines whether the end joint center of mass has reached the specified trajectory target point (the error with the trajectory target point is within the set range). If it has reached, it is completed; if it has not reached (the error with the trajectory target point is outside the set range), the axis with the largest joint position tracking error will be found from Axis 1 to Axis 6 and the parameters will be re-adjusted. If the tracking error after this adjustment is smaller than the first error, the overall robot arm servo system trajectory motion will be re-executed, and it will be determined whether the end joint center of mass has reached the trajectory target point. If the tracking error after this adjustment is larger than the first error, there are two options: either manually adjust the axis parameters or adjust the parameter setting value of the relay feedback, and then re-execute the parameter adjustment of the axis and determine the tracking error.

[0136] Figure 6 The joint position tracking error and the end joint mass center trajectory tracking error in the steps are different concepts. The joint position tracking error refers to the position tracking error of the position control loop based on the motor encoder position feedback running inside the joint servo motor driver; while the end joint mass center trajectory tracking error refers to the error between the spatial position coordinates of the mass center in the physical space of the xyz coordinate system and the spatial position coordinates of the trajectory instruction at the end point when the position trajectory of the mass center reaches the end point under the action of the motion trajectory instruction of the robot servo system.

[0137] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for optimizing the overall performance of a robot arm servo system, wherein the robot arm servo system comprises at least two axis joint servo modules connected in sequence; characterized in that: The performance tuning method comprises the following steps: S101: Establishing a digital twin system of a robotic arm composed of digital twin models of servo modules of each joint of the robotic arm; S102: Executing a performance feedback action of the robotic arm servo system in the digital twin system; collecting expected result data and actual result data of the digital twin system executing the performance feedback action, wherein the digital twin system collects operating parameters of each servo system uploaded by the robotic arm servo system in real time during operation, and performs virtual follow-up according to the collected operating parameters; S103: deriving grey correlation data based on the expected result data and the actual result data; wherein the grey correlation data includes: position tracking error e of the shaft joint servo module performing the performance feedback action; θ , Position loop gain value K p , speed loop gain value K v , speed loop integral gain value K i and the speed error e w ; The position tracking error e θ and speed error e w Derived based on the comparison between the expected result data and the actual result data; S104: Evaluating the comprehensive performance of each of the axis joints using a grey correlation algorithm based on the grey correlation data; S105: Optimizing the axis joint servo module whose comprehensive performance does not meet the performance index requirements.

2. The performance tuning method according to claim 1, characterized in that: The tuning method further includes S106: repeating S102-S105 to further tune the robot arm servo system.

3. The performance tuning method according to claim 1, characterized in that: The method for evaluating the comprehensive performance of each of the axis joint servo modules based on the grey correlation algorithm is specifically as follows: Let the target matrix be: X0=(x0(1),x0(2),x0(3),x0(4),x0(5)) The test results of the position tracking error of the robot arm servo system are expressed in the form of an orthogonal matrix as follows: X i =(x i (1), x i (2), x i (3), x i (4), x i (5)), i=1,2,…, n; wherein n represents the number of the axis joint servo modules; Then the grey correlation coefficient between the test result and the target value is expressed as: Where γ represents X i The degree of association with X0 at point k, the mean of which is the grey association degree, is shown in the following formula: Among them, k=1,2,…,5; i=1,2,…,n; ρ is a constant.

4. The performance tuning method according to claim 1, characterized in that: The data for building the twin model include: θ(t): the angle value of the axis joint servo module that changes with time in real time; ω(t): the real-time speed value of the axis joint servo module that changes with time; i(t): the current value of the axis joint servo module that changes with time in real time; α(t): the real-time acceleration value of the axis joint servo module that changes with time.

5. The performance tuning method according to claim 1, characterized in that: The data for building the twin model also includes the following: visual sensor information, torque sensor information and safety sensor information of the axis joint servo module.

6. The performance tuning method according to claim 1, characterized in that: The digital twin system is also used to collect the robot arm motion equation of the performance feedback action to generate an instruction trajectory.

7. The performance tuning method according to claim 6, characterized in that: The digital twin system is provided with a graphical interface; the graphical interface is used to display the posture change trajectory of each axis joint servo module in the xyz space and the instruction trajectory.

8. The performance tuning method according to claim 7, characterized in that: The posture change trajectory and the instruction trajectory are described based on the center of mass point trajectory.

9. The performance tuning method according to claim 1, characterized in that: The process before S101 also includes an initial parameter adjustment process; The initial parameter adjustment process comprises the following steps: S201: Initializing and adjusting the control parameters of the robot arm servo system; S202: initializing and adjusting the control parameters of the axis joint servo modules in the robot arm servo system in sequence from the end to the root; S203: The robot arm servo system executes the set trajectory movement; S204: Based on the digital twin system, compare whether the end effector of the robot arm reaches the specified target point. If yes, the initial parameter adjustment process is completed. If not, perform the following steps: S301: Selecting the axis joint servo module with the largest tracking error based on the digital twin system for initialization tuning; S302: Whether the tracking error of the axis joint servo module after initialization and tuning decreases, if so, jump to S203, if not, adjust the relay feedback parameter value of the axis joint servo module with the largest tracking error or manually set the axis control parameter of the axis joint servo module with the largest tracking error; After manually setting the axis control parameters of the axis joint servo module with the largest tracking error, jump to S301.

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