Joint motor control method and device, robot and storage medium
Through dynamic programming controller and neural network model, the robot joint motor control is optimized, and the robot's joint motor is insufficient, and the robot's ability to complete complex actions is improved.
Patent Information
- Application Number
- CN202410047561.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-01-11
- Publication Date
- 2025-07-11
AI Technical Summary
The robot's joint motor is poor in robustness and adaptability during control, resulting in poor dynamic response capabilities and inability to complete complex actions.
The dynamic programming controller is used to combine the neural network model to optimize the output of variables on the current loop system of the joint motor, and control the current and voltage through the intersection and straight axis to improve the robustness and adaptability of the joint motor.
It improves the control accuracy of joint motors and the dynamic response capabilities of the robot, and enhances the robot's ability to complete complex actions.
Smart Images
Figure CN120301264A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the technical field of robotics, and particularly to a method and apparatus for controlling a joint motor, a robot, and a storage medium. Background Art
[0002] In recent years, the technology of robotics has been continuously developing, becoming more and more intelligent and automated, and the richness, stability, and flexibility of movements have been improved to varying degrees. A bionic robot has multiple joints, and each joint is provided with a joint motor. The joint motor can drive the relative movement of the parts on both sides of the joint. It is precisely by the movement of these joint motors that the robot can complete various actions. However, in related technologies, the robustness and adaptability of the joint motors of the robot during control are relatively poor, resulting in poor dynamic response capabilities of the joint motors, and the robot cannot complete relatively complex actions. Summary of the Invention
[0003] To overcome the problems existing in related technologies, embodiments of the present disclosure provide a method and apparatus for controlling a joint motor, a robot, and a storage medium to solve the defects in related technologies.
[0004] According to a first aspect of an embodiment of the present disclosure, a method for controlling a joint motor is provided. The method includes:
[0005] Determine a quadrature-axis control current according to an expected joint torque;
[0006] Input the quadrature-axis control current, the direct-axis control current, the quadrature-axis actual current and the direct-axis actual current fed back by the joint motor into a dynamic programming controller to obtain a quadrature-axis control voltage and a direct-axis control voltage output by the dynamic programming controller, where the dynamic programming controller is used to output an optimal control variable for the current-loop system of the joint motor based on a neural network model;
[0007] Control the movement of the joint motor according to the quadrature-axis control voltage and the direct-axis control voltage.
[0008] In a possible embodiment of the present disclosure, the determining a quadrature-axis control current according to an expected joint torque includes:
[0009] Determine a quadrature-axis control current according to an expected joint torque, the amplitude of the permanent magnet flux linkage of the joint motor, and the number of pole pairs.
[0010] In a possible embodiment of the present disclosure, the dynamic programming controller is used to output an optimal control variable for the Hamiltonian function of the current-loop system of the joint motor based on a neural network model, where the Hamiltonian function of the current-loop system is related to the cost function of the current-loop system.
[0011] In a possible embodiment of the present disclosure, the dynamic programming controller is configured to predict the network parameters of the value function network in the neural network model based on the Hamiltonian function, and predict the parameters of the policy network and the optimal control variables in the neural network model based on the network parameters of the value function network.
[0012] In a possible embodiment of the present disclosure, the method further includes:
[0013] Input the desired joint state in the motion control instruction and the actual joint state feedback by the joint motor into an impedance controller to obtain the desired joint torque output by the impedance controller.
[0014] In a possible embodiment of the present disclosure, the desired joint state includes a desired joint position and a desired joint velocity; and / or,
[0015] The actual joint state includes an actual joint position and an actual joint velocity.
[0016] In a possible embodiment of the present disclosure, the method further includes:
[0017] Obtain the actual joint state, the actual joint torque, the quadrature-axis actual current, and the direct-axis actual current feedback by the joint motor.
[0018] According to a second aspect of the embodiments of the present disclosure, there is provided a joint motor control device, the device includes:
[0019] A current module, configured to determine a quadrature-axis control current according to a desired joint torque;
[0020] A voltage module, configured to input the quadrature-axis control current, the direct-axis control current, the quadrature-axis actual current, and the direct-axis actual current feedback by the joint motor into a dynamic programming controller to obtain the quadrature-axis control voltage and the direct-axis control voltage output by the dynamic programming controller, wherein the dynamic programming controller is configured to output an optimal control variable for a current loop system of the joint motor based on a neural network model;
[0021] A control module, configured to control the motion of the joint motor according to the quadrature-axis control voltage and the direct-axis control voltage.
[0022] In a possible embodiment of the present disclosure, the current module is configured to:
[0023] Determine a quadrature-axis control current according to a desired joint torque, the amplitude of the permanent magnet flux linkage of the joint motor, and the number of pole pairs.
[0024] In a possible embodiment of the present disclosure, the dynamic programming controller is configured to output an optimal control variable based on a neural network model for the Hamiltonian function of the current loop system of the joint motor, wherein the Hamiltonian function of the current loop system is related to the cost function of the current loop system.
[0025] In a possible embodiment of the present disclosure, the dynamic programming controller is configured to predict the network parameters of the value function network in the neural network model based on the Hamiltonian function, and predict the parameters of the policy network in the neural network model and the optimal control variable based on the network parameters of the value function network.
[0026] In a possible embodiment of the present disclosure, the device further includes a torque module, and the torque module is configured to:
[0027] Input the desired joint state in the motion control instruction and the actual joint state feedback by the joint motor into an impedance controller to obtain the desired joint torque output by the impedance controller.
[0028] In a possible embodiment of the present disclosure, the desired joint state includes a desired joint position and a desired joint velocity; and / or,
[0029] The actual joint state includes an actual joint position and an actual joint velocity.
[0030] In a possible embodiment of the present disclosure, the device further includes an acquisition module, and the acquisition module is configured to:
[0031] Acquire the actual joint state, the actual joint torque, the quadrature-axis actual current, and the direct-axis actual current feedback by the joint motor.
[0032] According to a third aspect of the embodiments of the present disclosure, a robot is provided, which includes a memory and a processor. The memory is configured to store computer instructions that can be run on the processor, and the processor is configured to implement the joint motor control method described in the first aspect when executing the computer instructions.
[0033] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method described in the first aspect is implemented.
[0034] The technical solutions provided by the embodiments of the present disclosure may include the following beneficial effects:
[0035] The joint motor control method provided by the embodiments of the present disclosure first determines the quadrature-axis control current according to the desired joint torque; then inputs the quadrature-axis control current, the direct-axis control current, the actual quadrature-axis current and the actual direct-axis current fed back by the joint motor into a dynamic programming controller to obtain the quadrature-axis control voltage and the direct-axis control voltage output by the dynamic programming controller; finally, controls the joint motor to move according to the quadrature-axis control voltage and the direct-axis control voltage. Since the dynamic programming controller is used to output the optimal control variables for the current loop system of the joint motor based on the neural network model, the robustness and self-adaptability of the joint motor in the control process can be improved, and the dynamic response ability of the robot, as well as the control accuracy and motion complexity of the robot can be improved. Description of the Drawings
[0036] The drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present invention, and are used together with the specification to explain the principles of the present invention.
[0037] Figure 1 is a flowchart of the joint motor control method shown in an exemplary embodiment of the present disclosure;
[0038] Figure 2 is a flowchart of the joint motor control method shown in an exemplary embodiment of the present disclosure;
[0039] Figure 3 is a schematic structural diagram of the joint motor control device shown in an exemplary embodiment of the present disclosure;
[0040] Figure 4 is a structural block diagram of the robot shown in an exemplary embodiment of the present disclosure. Detailed Embodiments
[0041] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are merely examples of the devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.
[0042] The terms used in the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure. The singular forms "a", "the" and "said" used in the present disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.
[0043] It should be understood that although the terms first, second, third, etc. may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this disclosure, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining".
[0044] In recent years, the technology of robotics has been continuously developing, becoming more and more intelligent and automated, and the richness, stability, and flexibility of movements have all been improved to varying degrees. A bionic robot has multiple joints, and each joint is equipped with a joint motor. The joint motor can drive the parts on both sides of the joint to perform relative movements. It is precisely by the movements of these joint motors that the robot can complete various actions. However, in related technologies, the robustness and adaptability of the joint motors of the robot are relatively poor during control, resulting in poor dynamic response capabilities of the joint motors, and the robot cannot complete relatively complex actions.
[0045] Based on this, in a first aspect, at least one embodiment of the present disclosure provides a method for controlling a joint motor. Please refer to the attached Figure 1 , which shows the flow of this method, including steps S101 to S103.
[0046] Among them, this method can be applied to a robot, such as a legged robot like a bipedal robot or a quadruped robot; the robot has multiple joints, and each joint is provided with a joint motor. This method can be applied to each joint motor of the robot, that is, using this method to drive the joint motor to move to complete the desired joint state obtained by the upper-level motion control according to the desired action.
[0047] In step S101, a quadrature-axis control current is determined according to the desired joint torque.
[0048] Among them, the desired joint torque can be determined in advance in the following manner: input the desired joint state in the motion control instruction and the actual joint state feedback by the joint motor into an impedance controller to obtain the desired joint torque output by the impedance controller.
[0049] The desired joint state can be the desired state of the joints controlled by this method as determined by the upper-level motion control according to the desired motion of the robot. When the joint motor is in motion, it can feedback the joint state at a certain frequency, and this state is the actual joint state. Therefore, before performing this step, the actual joint state feedback by the joint motor can be obtained. Exemplarily, the desired joint state includes the desired joint position and the desired joint velocity, and the actual joint state includes the actual joint position and the actual joint velocity. The position error can be determined based on the desired joint position and the actual joint position, and the velocity error can be determined based on the desired joint velocity and the actual joint velocity. Then, the position error and the velocity error are input into the impedance controller; the impedance controller can, based on the position error and the velocity error, combine its internal parameters and the feedforward torque, etc., to determine the desired joint torque.
[0050] Exemplarily, in this step, the quadrature-axis control current can be determined based on the desired joint torque, as well as the amplitude of the permanent magnet flux linkage and the number of pole pairs of the joint motor. For example, the quadrature-axis control current is determined according to the following formula (1):
[0051]
[0052] In the above formula, is the desired joint torque, is the quadrature-axis control current, ψ f is the amplitude of the permanent magnet flux linkage, n p is the number of pole pairs.
[0053] In step S102, the quadrature-axis control current, the direct-axis control current, as well as the quadrature-axis actual current and the direct-axis actual current feedback by the joint motor are input into the dynamic programming controller to obtain the quadrature-axis control voltage and the direct-axis control voltage output by the dynamic programming controller, where the dynamic programming controller is used to output the optimal control variables for the current loop system of the joint motor based on the neural network model.
[0054] Optionally, before performing this step, the direct-axis actual current and the quadrature-axis actual current feedback by the joint motor can be obtained. The direct-axis actual current refers to the actual current value reached by the joint motor on the direct axis, and the quadrature-axis actual current refers to the actual current value reached by the joint motor on the quadrature axis.
[0055] First, an introduction to the current loop system of the joint motor is given.
[0056] The current equations of the shutdown motor on the d-q axes are shown in the following equation (1):
[0057]
[0058]
[0059] In the above formula, np is the number of pole pairs, ψ f is the amplitude of the permanent magnet flux linkage, R s is the stator resistance, L is the stator inductance, ω r is the mechanical angular velocity, i d is the direct-axis current, i q is the quadrature-axis current, is the derivative of the direct-axis current with respect to time, is the derivative of the quadrature-axis current with respect to time, U d is the direct-axis voltage, U q is the quadrature-axis voltage.
[0060] The tracking errors of the direct axis and the quadrature axis are defined as shown in Equation 2 below:
[0061]
[0062] In the above formula, e d is the direct-axis tracking error, e q is the quadrature-axis tracking error, is the direct-axis control current, is the quadrature-axis control current, i d is the direct-axis actual current, i q is the quadrature-axis actual current.
[0063] Combining Equation 1 and Equation 2 above, the following Equation 3 can be obtained:
[0064]
[0065]
[0066] Converting the above Equation 3 to the following Equation 4:
[0067]
[0068] In the above formula:
[0069] x = [x1 x2] T = [e d e q T u = [u1 u2] T = [U d U q T
[0070] h(x) = [h1(x) h2(x)] T = [y1y2] T = [e d e q T
[0071] g(x) = [g1(x) g2(x)] T = [-1 / L -1 / L] T
[0072]
[0073] Next, determine the cost function of the current loop system shown in Equation 4 above. Since the control objective of dynamic programming for it not only includes current tracking but also minimizing the cost function. Exemplarily, the cost function can be as shown in Equation 2 below:
[0074]
[0075] In the above formula, Q(x) is a positive definite function, R is a symmetric positive semi - definite constant matrix, and u is a matrix composed of the quadrature - axis voltage and the direct - axis voltage.
[0076] Define Then the Hamiltonian function of the current loop system shown in Equation 4 above can be as shown in Equation 3 below:
[0077]
[0078] The optimal value function J * Satisfies Under the optimal control variable u * The Hamilton - Jacobi - Bellman (HJB) equation is transformed into the form shown in Equation 4 below:
[0079]
[0080] Furthermore, the optimal control variable of the current loop system shown in Equation 4 above can be obtained as shown in Equation 5 below:
[0081]
[0082] Finally, introduce the process by which the dynamic programming controller outputs the optimal control variable for the current loop system of the joint motor based on the neural network model.
[0083] The dynamic programming controller can be used to output the optimal control variable for the Hamiltonian function of the current loop system of the joint motor based on the neural network model, where the Hamiltonian function of the current loop system is related to the cost function of the current loop system.
[0084] For example, the cost function can be approximated by the Critic neural network shown in Equation 6 below:
[0085] J = W T φ(x)+ε(x)
[0086] In the above formula, W is the unknown neural network weight, φ(x) is the activation function, and ε(x) is the approximation error.
[0087] Substituting the above formula (6) into the above formula (3), the Hamiltonian function of the current loop system shown in the above equation (4) can be transformed into the form shown in the following formula (7):
[0088]
[0089] In the above formula, is the differential of f + gu, and f + gu is f(x) + g(x)u in equation (4); is the differential of φ(x) with respect to time t.
[0090] It can be understood that as the number of neural network nodes approaches infinity, ε(x) will approach 0. Since W is unknown, the cost function J can be approximately expressed in the form shown in the following formula (8):
[0091]
[0092] In the above formula, is the estimated value of W. Furthermore, the Hamiltonian function can be transformed into the form shown in the following formula (9):
[0093]
[0094] Define the approximation error of the Critic network as The following formula (10) can be obtained:
[0095]
[0096] Optionally, the dynamic programming controller is used to predict the network parameters of the value function network Critic in the neural network model based on the Hamiltonian function, and predict the parameters of the policy network Actor and the optimal control variable in the neural network model based on the network parameters of the value function network.
[0097] For example, choosing the goal is to minimize the quadratic error Then the normalized gradient algorithm shown in the following formula (11) can be used to adjust the network weights (i.e., network parameters) of the Critic:
[0098]
[0099] In the above formula, a1 is an adjustable parameter, used for normalization.
[0100] According to the above equations 5 and 6, the optimal control variable of the current loop system shown in the above equation 4 can be expressed as: When the number of neural network nodes tends to infinity, tends to 0. Since W is unknown, the optimal control variable can be approximated by the Actor neural network according to the following formula 12:
[0101]
[0102] In the above formula, is the network weight W of the Actor neural network a The estimated value of .
[0103] The adaptive law of the Actor network is shown in Equation 13:
[0104]
[0105] In the above formula, a1 is an adjustable parameter, g is the same as g(x) in the above equation 4.
[0106] In summary, the dynamic programming controller can predict the network parameters of the value function network Critic in the neural network model based on the Hamiltonian function through the above formula 11; the parameters of the strategy network Actor in the neural network model can be predicted based on the network parameters of the value function network by combining the above formula 12 and the above formula 13. and the optimal control variable Make predictions. Among them, the optimal control variable That is, the optimal control variables (ie, the quadrature-axis control voltage and the direct-axis control voltage) of the current loop system shown in the above equation 4 output by the dynamic programming controller.
[0107] In step S103, the joint motor is controlled to move according to the quadrature-axis control voltage and the direct-axis control voltage.
[0108] The joint motor control method provided by the embodiment of the present disclosure first determines the quadrature axis control current according to the expected joint torque; then the quadrature axis control current and the direct axis control current, as well as the quadrature axis actual current and the direct axis actual current fed back by the joint motor are input into the dynamic programming controller to obtain the quadrature axis control voltage and the direct axis control voltage output by the dynamic programming controller; finally, according to the quadrature axis control voltage and the direct axis control voltage, the joint motor is controlled to move. Since the dynamic programming controller is used to output the optimal control variables of the current loop system of the joint motor based on the neural network model, the robustness and adaptability of the joint motor in the control process can be improved, and the dynamic response capability of the robot, as well as the robot control accuracy and motion complexity can be improved.
[0109] Please refer to the attached Figure 2 , which exemplarily shows the flowchart of the joint motor control method obtained by combining the above-mentioned multiple embodiments. Among them, the upper-level motion control issues the desired joint position θ d , the desired joint speed v d , the impedance controller parameters K p , K d and the feedback torque τ ff . Combining the actual joint position θ and the actual joint speed v feedback by the joint motor, the desired joint torque is obtained through the impedance controller The quadrature-axis control current is obtained through the relationship between torque and current (such as the relationship shown in Equation 1 above) Combined with the preset direct-axis control current (such as ) and the quadrature-axis actual current i q , direct-axis actual current i d fed back by the joint motor, the quadrature-axis and direct-axis control voltages are obtained through the adaptive dynamic programming controller The three-phase voltage is obtained through pulse width modulation and the motor is driven to operate by the inverter
[0110] It should be understood that the same symbols appearing in this disclosure represent the same meaning of parameters
[0111] According to the second aspect of the embodiments of the present disclosure, a joint motor control device is provided. Please refer to the attached Figure 3 , the device includes:
[0112] A current module 301, configured to determine the quadrature-axis control current according to the desired joint torque
[0113] A voltage module 302, configured to input the quadrature-axis control current, the direct-axis control current, and the quadrature-axis actual current and the direct-axis actual current fed back by the joint motor into the dynamic programming controller, and obtain the quadrature-axis control voltage and the direct-axis control voltage output by the dynamic programming controller, where the dynamic programming controller is used to output the optimal control variables for the current loop system of the joint motor based on the neural network model
[0114] A control module 303, configured to control the movement of the joint motor according to the quadrature-axis control voltage and the direct-axis control voltage
[0115] In a possible embodiment of the present disclosure, the current module is used for:
[0116] Determine the quadrature-axis control current according to the desired joint torque, the permanent magnet flux linkage amplitude of the joint motor, and the number of pole pairs
[0117] In a possible embodiment of the present disclosure, the dynamic programming controller is configured to output an optimal control variable based on a neural network model for a Hamiltonian function of a current loop system of a joint motor, wherein the Hamiltonian function of the current loop system is related to a cost function of the current loop system.
[0118] In a possible embodiment of the present disclosure, the dynamic programming controller is configured to predict network parameters of a value function network in the neural network model based on the Hamiltonian function, and predict parameters of a policy network in the neural network model and the optimal control variable based on the network parameters of the value function network.
[0119] In a possible embodiment of the present disclosure, the device further includes a torque module, and the torque module is configured to:
[0120] Input a desired joint state in the motion control instruction and an actual joint state feedback by the joint motor into an impedance controller to obtain the desired joint torque output by the impedance controller.
[0121] In a possible embodiment of the present disclosure, the desired joint state includes a desired joint position and a desired joint velocity; and / or,
[0122] The actual joint state includes an actual joint position and an actual joint velocity.
[0123] In a possible embodiment of the present disclosure, the device further includes an acquisition module, and the acquisition module is configured to:
[0124] Acquire the actual joint state, the actual joint torque, the quadrature-axis actual current, and the direct-axis actual current feedback by the joint motor.
[0125] In a third aspect, at least one embodiment of the present disclosure provides a robot. Please refer to the appendix Figure 4 , which shows the structure of the robot. The robot includes a memory and a processor. The memory is configured to store computer instructions that can be run on the processor, and the processor is configured to control a joint motor based on the method according to any one of the first aspect when executing the computer instructions.
[0126] In a fourth aspect, at least one embodiment of the present disclosure provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the method according to any one of the first aspect is implemented.
[0127] Other embodiments of the present disclosure will be readily apparent to those skilled in the art in view of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include known common general knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and examples are only to be considered as exemplary, and the true scope and spirit of the present disclosure are pointed out by the following claims.
[0128] It should be understood that the present disclosure is not limited to the exact structures already described and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.
Claims
1. A method for controlling a joint motor, characterized in that, The method includes: Determining a quadrature-axis control current according to an expected joint torque; Inputting the quadrature-axis control current, the direct-axis control current, the actual quadrature-axis current and the actual direct-axis current fed back by the joint motor into a dynamic programming controller to obtain a quadrature-axis control voltage and a direct-axis control voltage output by the dynamic programming controller, wherein the dynamic programming controller is used to output optimal control variables for the current loop system of the joint motor based on a neural network model; Controlling the joint motor to move according to the quadrature-axis control voltage and the direct-axis control voltage.
2. The joint motor control method according to claim 1, wherein The determining a quadrature-axis control current according to an expected joint torque includes: Determining a quadrature-axis control current according to an expected joint torque, the amplitude of the permanent magnet flux linkage of the joint motor, and the number of pole pairs.
3. The joint motor control method according to claim 1, wherein The dynamic programming controller is used to output optimal control variables for the Hamiltonian function of the current loop system of the joint motor based on a neural network model, wherein the Hamiltonian function of the current loop system is related to the cost function of the current loop system.
4. The joint motor control method according to claim 3, wherein The dynamic programming controller is used to predict the network parameters of the value function network in the neural network model based on the Hamiltonian function, and predict the parameters of the policy network in the neural network model and the optimal control variables based on the network parameters of the value function network.
5. The joint motor control method according to claim 1, wherein It is characterized in that The method further includes: Inputting the expected joint state in the motion control instruction and the actual joint state fed back by the joint motor into an impedance controller to obtain the expected joint torque output by the impedance controller.
6. The joint motor control method according to claim 5, characterized in that, The expected joint state includes an expected joint position and an expected joint velocity; and / or The actual joint state includes an actual joint position and an actual joint velocity.
7. The joint motor control method according to claim 5, wherein The method further includes: Obtaining the actual joint state, the actual joint torque, the actual quadrature-axis current and the actual direct-axis current fed back by the joint motor.
8. An articulated motor control device, characterized in that, The device includes: A current module for determining a quadrature-axis control current according to an expected joint torque; A voltage module for inputting the quadrature-axis control current, the direct-axis control current, the actual quadrature-axis current and the actual direct-axis current fed back by the joint motor into a dynamic programming controller to obtain a quadrature-axis control voltage and a direct-axis control voltage output by the dynamic programming controller, wherein the dynamic programming controller is used to output optimal control variables for the current loop system of the joint motor based on a neural network model; A control module for controlling the joint motor to move according to the quadrature-axis control voltage and the direct-axis control voltage.
9. A robot, characterized in that, The robot includes a memory and a processor, the memory is used to store computer instructions that can be run on the processor, and the processor is used to implement the joint motor control method according to any one of claims 1 to 7 when executing the computer instructions.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, the method according to any one of claims 1 to 7 is implemented.