Control Method, Device, Electronic Device and Storage Medium of Flexible Manipulator

Abstract

The embodiments of the present application provide a control method, device, electronic device, and storage medium for a flexible robotic arm, relating to the technical field of robotic arm control. The method includes: obtaining the dynamic models of multiple flexible robotic arms; for a target robotic arm among the multiple flexible robotic arms, obtaining the state observation variables of the target robotic arm according to the dynamic model of the target robotic arm; calculating the target joint angles of the target robotic arm according to the communication topology information among the multiple flexible robotic arms, the first joint angles of other flexible robotic arms, and the second joint angles of the target robotic arm; controlling the electromagnetic torque of the target robotic arm according to the dynamic model of the target robotic arm, the state observation variables of the target robotic arm, and the target joint angles of the target robotic arm, so as to adjust the joint angles of the target robotic arm to the target joint angles. The embodiments of the present application can improve the control accuracy in the control process of multiple flexible robotic arms.

Description

Technical Field

[0001] This application relates to the technical field of robotic arm control, and particularly to a control method, device, electronic device, and storage medium for a flexible robotic arm. Background Art

[0002] The key components of a flexible robotic arm are made of lightweight flexible materials, so it has comprehensive advantages such as good compliance, strong adaptability, high safety, and excellent flexibility, and is widely used in fields such as electronics manufacturing, aerospace, and nuclear energy. For example, the joints of a collaborative robot contain series elastic actuators, and the joints are flexible, enabling good force-position hybrid compliance control for human-robot interaction. Multiple flexible robotic arms can perform more complex tasks and carry a greater load compared to a single flexible robotic arm. The control strategy of multiple flexible robotic arms directly determines their control performance. Therefore, designing a suitable control algorithm is very important for improving the control accuracy of flexible robotic arms and reducing the response time.

[0003] Existing control methods for multiple flexible robotic arms are usually applied to synchronous sampling scenarios, that is, the communication cycles of each flexible robotic arm are the same. However, during actual operation, the communication cycles and communication times between each flexible robotic arm and between every two flexible robotic arms may all be different, that is, asynchronous sampling. Therefore, when multiple flexible robotic arms are sampled asynchronously, using existing control methods for multiple flexible robotic arms easily results in low control accuracy. Summary of the Invention

[0004] The main purpose of the embodiments of this application is to propose a control method, device, electronic device, and storage medium for a flexible robotic arm, aiming to solve the problem of improving the control accuracy during the control process of multiple flexible robotic arms.

[0005] To achieve the above object, in the first aspect of the embodiments of this application, a control method for a flexible robotic arm is proposed. The method includes:

[0006] Obtain the dynamic models of multiple flexible robotic arms;

[0007] For a target robotic arm among the multiple flexible robotic arms, based on the dynamic model of the target robotic arm, obtain the state observation quantity of the target robotic arm, where the state observation quantity is used to describe the motion state of the target robotic arm, and the target robotic arm is any one of the multiple flexible robotic arms;

[0008] Calculate the target joint angle of the target robotic arm based on the communication topology information among multiple said flexible robotic arms, the first joint angles of other flexible robotic arms, and the second joint angle of the target robotic arm. The other flexible robotic arms are all the flexible robotic arms among the multiple flexible robotic arms except the target robotic arm. The first joint angle is the joint angle of the other flexible robotic arms at the communication moment between the other flexible robotic arms and the target robotic arm, and the second joint angle is the joint angle of the target robotic arm at the data sampling moment of the target robotic arm.

[0009] Control the electromagnetic torque of the target robotic arm according to the dynamic model of the target robotic arm, the state observation quantity of the target robotic arm, and the target joint angle of the target robotic arm, so as to adjust the joint angle of the target robotic arm to the target joint angle.

[0010] To achieve the above object, a second aspect of the embodiments of the present application provides a control device for a flexible robotic arm. The device includes:

[0011] An acquisition module, configured to acquire the dynamic models of multiple flexible robotic arms;

[0012] An observation module, configured to, for a target robotic arm among the multiple flexible robotic arms, obtain the state observation quantity of the target robotic arm according to the dynamic model of the target robotic arm. The state observation quantity is used to describe the motion state of the target robotic arm, and the target robotic arm is any one of the multiple flexible robotic arms;

[0013] A calculation module, configured to calculate the target joint angle of the target robotic arm based on the communication topology information among multiple said flexible robotic arms, the first joint angles of other flexible robotic arms, and the second joint angle of the target robotic arm. The other flexible robotic arms are all the flexible robotic arms among the multiple flexible robotic arms except the target robotic arm. The first joint angle is the joint angle of the other flexible robotic arms at the communication moment between the other flexible robotic arms and the target robotic arm, and the second joint angle is the joint angle of the target robotic arm at the data sampling moment of the target robotic arm.

[0014] A control module, configured to control the electromagnetic torque of the target robotic arm according to the dynamic model of the target robotic arm, the state observation quantity of the target robotic arm, and the target joint angle of the target robotic arm, so as to adjust the joint angle of the target robotic arm to the target joint angle.

[0015] To achieve the above object, a third aspect of the embodiments of the present application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the control method of the flexible robotic arm described in the first aspect above is implemented.

[0016] To achieve the above object, a fourth aspect of the embodiments of the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the control method of the flexible robotic arm described in the first aspect above is implemented.

[0017] The control method, device, electronic device, and storage medium of the flexible robotic arm proposed in the present application obtain the dynamic models of multiple flexible robotic arms. For the target robotic arm among the multiple flexible robotic arms, according to the dynamic model of the target robotic arm, the state observation quantities of the target robotic arm are obtained. Then, according to the communication topology information between the multiple flexible robotic arms, the first joint angles of other flexible robotic arms, and the second joint angle of the target robotic arm, the target joint angle of the target robotic arm is calculated. Subsequently, according to the dynamic model of the target robotic arm, the state observation quantities of the target robotic arm, and the target joint angle of the target robotic arm, the electromagnetic torque of the target robotic arm is controlled to adjust the joint angle of the target robotic arm to the target joint angle. Through the above steps, the target joint angle of the target robotic arm can be determined according to the joint angles of other flexible robotic arms (collected at the communication moment between other flexible robotic arms and the target robotic arm) and the joint angle of the target robotic arm (collected at the data sampling moment of the target robotic arm). Furthermore, the joint angle of the target robotic arm can be controlled according to the target joint angle, which can be applied to the operation scenario of asynchronous sampling of multiple flexible robotic arms and improve the control accuracy of the control process of multiple flexible robotic arms. Description of the Drawings

[0018] Figure 1 is a flowchart of the control method of the flexible robotic arm provided by the embodiments of the present application;

[0019] Figure 2 is a schematic structural diagram of the control device of the flexible robotic arm provided by the embodiments of the present application;

[0020] Figure 3 is a schematic hardware structure diagram of the electronic device provided by the embodiments of the present application. Detailed Embodiments

[0021] In order to make the purpose, technical solutions, and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0022] It should be noted that although the functional modules are divided in the device schematic diagram and the logical sequence is shown in the flowchart, in some cases, the steps shown or described can be executed in a different module division from that in the device or a different sequence from that in the flowchart. Terms such as "first" and "second" in the description, claims and the above-mentioned drawings are used to distinguish similar objects and do not necessarily describe a specific order or sequence.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0024] First, several nouns involved in this application are analyzed as follows:

[0025] Flexible robotic arm: It is a robotic arm with deformable and flexible characteristics, usually composed of lightweight and soft materials (such as plastics, composite materials, etc.). Due to its flexible structure, the flexible robotic arm can deform during operation, has different dynamic characteristics from rigid robotic arms, and can adapt to more complex working environments.

[0026] Dynamic model: It is a mathematical model that describes the mutual relationships between forces, torques, accelerations and motion states among the joints, links and external loads of a flexible robotic arm during motion. It can be used to analyze and control the dynamic behavior of the flexible robotic arm to ensure that the flexible robotic arm can perform tasks stably and accurately.

[0027] Joint angle: It refers to the angle of each joint (or link) of a flexible robotic arm relative to a certain reference position or reference coordinate system. In the kinematic analysis and control of a flexible robotic arm, the joint angle is a very important parameter, which directly affects the posture of the flexible robotic arm and the position of the end effector. By precisely controlling and calculating the joint angles of the flexible robotic arm, the flexible robotic arm can perform various tasks.

[0028] At present, the optimization control methods for multiple flexible robotic arms generally include two parts. One is the design of a distributed optimizer, that is, by designing a distributed optimizer for each flexible robotic arm to obtain the optimal solution to the optimization problem. The other is the controller design, that is, to design a feedback controller according to the values solved by the distributed optimizer. Among them, in the design of the distributed optimizer, a sampling-type optimizer that only needs to transmit data periodically and has a light communication burden is generally adopted. However, the sampling-type optimizer can only be applied to synchronous sampling scenarios at present, that is, the communication cycles of each flexible robotic arm are the same. However, during the actual operation process, the communication cycles and communication times between each flexible robotic arm and between every two flexible robotic arms may all be different, that is, asynchronous sampling. And when the existing flexible robotic arm control methods are applied to the control scenarios of asynchronous sampling of multiple flexible robotic arms, there is a problem of low control accuracy.

[0029] Based on this, the embodiments of the present application provide a control method, device, electronic device and storage medium for flexible robotic arms, aiming to improve the control accuracy during the control process of multiple flexible robotic arms.

[0030] The control method, device, electronic device and storage medium for flexible robotic arms provided by the embodiments of the present application are specifically described through the following embodiments. First, the control method for flexible robotic arms in the embodiments of the present application is described.

[0031] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is to use a digital computer or a machine controlled by a digital computer to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results of theory, method, technology and application system.

[0032] The basic technologies of artificial intelligence generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. The software technologies of artificial intelligence mainly include several major directions such as computer vision technology, robotics technology, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0033] The control method of the flexible robotic arm provided by the embodiments of the present application relates to the technical field of robotic arm control. The control method of the flexible robotic arm provided by the embodiments of the present application can be applied to a terminal, or to a server side, or can also be software running on a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, or can also be configured as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks, and big data and artificial intelligence platforms; the software can be an application that implements the control method of the flexible robotic arm, etc., but is not limited to the above forms.

[0034] The present application can be used in numerous general or specific computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet-type devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, small computers, large computers, distributed computing environments including any of the above systems or devices, and so on. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0035] Figure 1 is a flowchart of the control method of the flexible robotic arm provided by the embodiments of the present application. As Figure 1 shown, the control method of the flexible robotic arm provided by the embodiments of the present application can be applied to an electronic device. Figure 1 The method in

[0036] Step 101, obtain the dynamic models of multiple flexible robotic arms.

[0037] When using multiple flexible robotic arms to cooperate in operations, the dynamic models of multiple flexible robotic arms can be obtained so as to subsequently analyze and control the actions of the multiple flexible robotic arms based on the dynamic models of the multiple flexible robotic arms. Assuming the number of flexible robotic arms is N, the dynamic model of each flexible robotic arm satisfies the following formula (1):

[0038] (1);

[0039] Among them, is the i inertia matrix of the th flexible robotic arm at the joint angle of is the i Coriolis force and centrifugal force terms of the th flexible robotic arm at the joint angular velocity of is the i th flexible robotic arm's gravity term at the joint angle of ; is the i th flexible robotic arm's external load at the joint angular velocity of ; is the torque of the flexible robotic arm, , is the proportionality coefficient, is the i th flexible robotic arm motor's rotation angle, is the i th flexible robotic arm motor's first motor parameter, is the i th flexible robotic arm motor's second motor parameter, is the i th flexible robotic arm's electromagnetic torque, is the joint angle of the flexible robotic arm.

[0040] Step 102: For the target robotic arm among the multiple flexible robotic arms, according to the dynamic model of the target robotic arm, obtain the state observation quantity of the target robotic arm, where the state observation quantity is used to describe the motion state of the target robotic arm, and the target robotic arm is any one of the multiple flexible robotic arms.

[0041] In the specific control process, any one of the multiple flexible robotic arms can be determined as the target robotic arm. Thus, based on the dynamic model of the target robotic arm, the motion state of the target robotic arm can be observed, and then the state observation quantity of the target robotic arm can be obtained. Among them, the dynamic model of the target robotic arm satisfies Equation (1). The state observation quantity is used to describe the motion state of the target robotic arm. In one example, the state observation quantity may include joint angle observation quantity, joint velocity observation quantity, joint acceleration observation quantity, and joint jerk observation quantity.

[0042] Step 103: Calculate the target joint angle of the target robotic arm based on the communication topology information among multiple flexible robotic arms, the first joint angles of other flexible robotic arms, and the second joint angle of the target robotic arm. The other flexible robotic arms are all flexible robotic arms except the target robotic arm among the multiple flexible robotic arms. The first joint angle is the joint angle of the other flexible robotic arms at the communication moment between the other flexible robotic arms and the target robotic arm, and the second joint angle is the joint angle of the target robotic arm at the data sampling moment of the target robotic arm.

[0043] Based on the communication topology information among multiple flexible robotic arms, the first joint angles of other flexible robotic arms, and the second joint angle of the target robotic arm, the target joint angle of the target robotic arm can be calculated. The communication topology information is used to describe the communication relationship among multiple flexible robotic arms. The other flexible robotic arms are all flexible robotic arms except the target robotic arm among the multiple flexible robotic arms. The first joint angle is the joint angle of the other flexible robotic arms at the communication moment between the other flexible robotic arms and the target robotic arm, and the second joint angle is the joint angle of the target robotic arm at the data sampling moment of the target robotic arm. Exemplarily, through the gradient descent method, the following formula (2) can be solved to obtain the optimal solution , and then the optimal solution is used as the target joint angle of the target robotic arm. Formula (2) satisfies:

[0044] (2);

[0045] where, is the first variable constructed for the joint angle of the target robotic arm i and can converge to the optimal solution , , is the function value of the local optimization function of , represents the gradient of is the communication relationship between the target robotic arm i and the j th flexible robotic arm among other flexible robotic arms, which can be determined according to the communication topology information among multiple flexible robotic arms, represents that the target robotic arm i and the j th flexible robotic arm can communicate with each other, represents that the target robotic arm i and the j th flexible robotic arm cannot communicate with each other, is the intermediate variable constructed for the target robotic arm i ​ is the data sampling moment of the target robotic arm, represents the communication moment between other flexible robotic arms and the target robotic arm, , represents the intermediate variable of the target robotic arm at the data sampling moment, is at the j th communication moment between the flexible robotic arm and the target robotic arm, and is the j th intermediate variable of the flexible robotic arm, i.e., the first joint angle, i.e., the second joint angle.

[0046] Step 104: Control the electromagnetic torque of the target robotic arm according to the dynamic model of the target robotic arm, the state observation quantity of the target robotic arm, and the target joint angle of the target robotic arm, so as to adjust the joint angle of the target robotic arm to the target joint angle.

[0047] The electromagnetic torque of the flexible robotic arm can affect the joint angle of the flexible robotic arm through the transmission system. Therefore, by controlling the electromagnetic torque of the target robotic arm, precise control of the joint angle of the target robotic arm can be achieved. Specifically, after obtaining the state observation quantity of the target robotic arm and the target joint angle of the target robotic arm, the electromagnetic torque of the target robotic arm can be determined according to the dynamic model of the target robotic arm, the state observation quantity of the target robotic arm, and the target joint angle of the target robotic arm, and then the motor of the target robotic arm can be controlled according to the target electromagnetic torque, so that the electromagnetic torque of the motor is adjusted to the target electromagnetic torque. In this way, the joint angle of the target robotic arm can be adjusted to the target joint angle, realizing the control of the target robotic arm.

[0048] It should be noted that each flexible robotic arm can be controlled with reference to the foregoing steps to complete the collaborative operation of multiple flexible robotic arms.

[0049] Steps 101 to 104 illustrated in the embodiments of the present application obtain the dynamic models of multiple flexible robotic arms. For a target robotic arm among the multiple flexible robotic arms, according to the dynamic model of the target robotic arm, the state observation quantities of the target robotic arm are obtained. Then, according to the communication topology information among the multiple flexible robotic arms, the first joint angles of other flexible robotic arms, and the second joint angle of the target robotic arm, the target joint angle of the target robotic arm is calculated. Subsequently, according to the dynamic model of the target robotic arm, the state observation quantities of the target robotic arm, and the target joint angle of the target robotic arm, the electromagnetic torque of the target robotic arm is controlled to adjust the joint angle of the target robotic arm to the target joint angle. Through the above steps, the target joint angle of the target robotic arm can be determined according to the joint angles of other flexible robotic arms (collected at the communication moment between other flexible robotic arms and the target robotic arm) and the joint angle of the target robotic arm (collected at the data sampling moment of the target robotic arm). Furthermore, the joint angle of the target robotic arm can be controlled according to the target joint angle, which is applicable to the operation scenario of asynchronous sampling of multiple flexible robotic arms and improves the control accuracy in the control process of multiple flexible robotic arms.

[0050] In some embodiments, for a target robotic arm among the multiple flexible robotic arms, obtaining the state observation quantities of the target robotic arm according to the dynamic model of the target robotic arm includes:

[0051] For a target robotic arm among the multiple flexible robotic arms, the dynamic model of the target robotic arm is subjected to conversion processing to obtain a processed dynamic model, and the conversion processing is used to convert the dynamic model of the target robotic arm into the solvable processed dynamic model;

[0052] Based on the processed dynamic model, preset observation parameters, and adaptive parameters, the motion state of the target robotic arm is observed to obtain the state observation quantities of the target robotic arm.

[0053] To ensure the effect of estimating the motion state of the target robotic arm, the dynamic model of the target robotic arm can be subjected to conversion processing to convert the dynamic model of the target robotic arm into a solvable dynamic model, thereby obtaining a processed dynamic model. Among them, the dynamic model of the target robotic arm satisfies Equation (1).

[0054] An observer refers to a system or algorithm used to monitor and estimate the state of a flexible robotic arm, which can infer the internal state of the flexible robotic arm by observing some known system outputs. Therefore, through a preset observer, based on the processed dynamic model, preset observation parameters, and adaptive parameters, the motion state of the target robotic arm can be observed, and the state observation quantities of the target robotic arm can be obtained. Due to the existence of observation uncertainty in the observer, it is assumed that the measured signal is only , where, is the measured joint angle signal, is the target robotic arm i 's joint angle (which can be estimated according to the processed dynamic model), is the measurement uncertainty, satisfying , is a set constant.

[0055] Then, the state observable of the target robotic arm can be obtained to satisfy Equation (3):

[0056] (3);

[0057] wherein, is the joint angle observable, is the joint velocity observable, is the joint acceleration observable, is the joint jerk observable, is the measured joint angle signal, is the second system matrix related to the system parameters of the target robotic arm, is the electromagnetic torque of the target robotic arm, is based on the joint angle observable , the joint velocity observable , the joint acceleration observable and the joint jerk observable to obtain the function value of the basis function of the fuzzy logic algorithm, is the adaptive parameter, , is the first preset observation parameter of the observer, is the second preset observation parameter of the observer, is the third preset observation parameter of the observer, is the fourth preset observation parameter of the observer, and the preset observation parameters include the first preset observation parameter , the second preset observation parameter , the third preset observation parameter and the fourth preset observation parameter .

[0058] Thus, the state observable of the target robotic arm can be obtained, facilitating subsequent control of the joint angle of the target robotic arm based on the state observable.

[0059] In some embodiments, the parameters of the dynamic model of the target robotic arm include the joint angle of the target robotic arm, the joint angular velocity of the target robotic arm, the motor angle of the target robotic arm, and the motor speed of the target robotic arm;

[0060] For the target robotic arm among the multiple flexible robotic arms, performing a conversion process on the dynamic model of the target robotic arm to obtain a processed dynamic model, including:

[0061] For the target robotic arm among the multiple flexible robotic arms, using a plurality of first preset variables to perform a conversion process on the dynamic model of the target robotic arm to obtain the first dynamic model of the target robotic arm, and the plurality of first preset variables are constructed according to the joint angle of the target robotic arm, the joint angular velocity of the target robotic arm, the motor angle of the target robotic arm, and the motor speed of the target robotic arm;

[0062] Using a fuzzy logic algorithm to approximately process the state parameter function in the first dynamic model to obtain a second dynamic model, where the state parameter function is used to describe multiple state parameters of the target robotic arm, and the approximation process is used to convert the state parameter function into a solvable function approximated to the state parameter function;

[0063] Substituting a second preset variable into the second dynamic model to obtain the processed dynamic model, and the second preset variable is constructed according to the first preset variable and the joint angle of the target robotic arm.

[0064] It is possible to perform a conversion process on the dynamic model of the target robotic arm to obtain a processed dynamic model, and the dynamic model of the target robotic arm satisfies Equation (1). Specifically, the plurality of first preset variables include joint angle state variables joint velocity state variables motor angle state variables and motor speed state variables joint angle state variables joint velocity state variables joint acceleration state variables and joint jerk state variables , and the plurality of first preset variables are constructed according to the joint angle of the target robotic arm, the joint angular velocity of the target robotic arm, the motor angle of the target robotic arm, and the motor speed of the target robotic arm. Let , , , , at this time, is the joint angle state variable, is the joint velocity state variable, is the motor angle state variable, is the motor speed state variable. Then, the dynamic model of the target robotic arm can be converted into Equation (4):

[0065] (4);

[0066] Among them, is the joint velocity state variable, is the joint acceleration state variable, is the motor velocity state variable, is the motor acceleration state variable, is the first system matrix related to the joint angle state variable ; is the second system matrix related to the system parameters of the target robotic arm, is the third system matrix related to the joint angle state variable and the joint velocity state variable ; is the fourth system matrix related to the joint angle state variable , the joint velocity state variable , the motor angle state variable , and the motor velocity state variable ; is the electromagnetic torque of the target robotic arm.

[0067] Since there are non-matching nonlinear functions in the second and fourth orders of Equation (4), a computational explosion problem will occur when using the backstepping method for controller design. Therefore, Equation (4) will be further transformed. That is, let , , , , among which, represents the joint angle state variable, which is a substitution for , represents the joint velocity state variable, which is a substitution for , represents the joint acceleration state variable, which is a substitution for , represents the joint jerk state variable, which is a substitution for .

[0068] Then Equation (4) can be further transformed into Equation (5), that is:

[0069] (5);

[0070] Among them, represents the joint angle state variable, represents the joint velocity state variable, represents the joint acceleration state variable, represents the joint jerk state variable, is the second system matrix related to the system parameters of the target robotic arm, is the electromagnetic torque of the target manipulator, It is the function value of the function related to the target robot model parameters and state.

[0071] Then, according to the all-wheel drive system model, Equation (5) can be written as Equation (6):

[0072] (6);

[0073] in, is the fourth-order derivative of the joint angle state variable, is the second system matrix related to the system parameters of the target manipulator, is the electromagnetic torque of the target manipulator, is the function value of the state parameter function. In this case, the state parameter function is a nonlinear unknown function.

[0074] Therefore, fuzzy logic algorithm can be used to approximate the state parameter function , we get Equation (7), which is a solvable function that is similar to the state parameter function:

[0075] (7);

[0076] in, is the fourth-order derivative of the joint angle state variable, is the second system matrix related to the system parameters of the target manipulator, is the electromagnetic torque of the target manipulator, is the function value of the basis function of the fuzzy logic algorithm, is the weight, is the residual error.

[0077] To ensure tracking control problem, ,in, is the second preset variable, is the joint angle state variable in the first preset variable, To target the robotic arm i The first variable constructed from the joint angle.

[0078] The processed kinetic model thus satisfies formula (8):

[0079] (8);

[0080] in, is the fourth derivative of the second preset variable, is the second system matrix related to the system parameters of the target manipulator, is the electromagnetic torque of the target manipulator, is the fourth-order derivative of the joint angle of the target manipulator, is the function value of the basis function of the fuzzy logic algorithm, is the weight, is the residual error.

[0081] In this way, the processed dynamic model can be obtained, so that based on the processed dynamic model, the motion state of the target robotic arm can be observed, and the state observation values of the target robotic arm can be obtained.

[0082] In some embodiments, the state observation values include joint angle observation values, joint velocity observation values, joint acceleration observation values, and joint jerk observation values.

[0083] Specifically, according to the dynamic model of the target robotic arm, the state observation values of the target robotic arm can be obtained. The state observation values can include multiple observation values for describing the motion state of the target robotic arm, namely, joint angle observation values, joint velocity observation values, joint acceleration observation values, and joint jerk observation values. Among them, the joint angle observation value is the estimated value of the joint angle state variable the joint velocity observation value is the estimated value of the joint velocity state variable the joint acceleration observation value is the estimated value of the joint acceleration state variable the joint jerk observation value is the estimated value of the joint jerk state variable In this way, through the above state observation values, the motion state of the target robotic arm can be more comprehensively reflected, which is convenient for subsequent control of the joint angle of the target robotic arm.

[0084] In some embodiments, according to the communication topology information between multiple flexible robotic arms, the first joint angle of other flexible robotic arms, and the second joint angle of the target robotic arm, calculating the target joint angle of the target robotic arm includes:

[0085] Through a preset optimizer, according to the communication topology information between multiple flexible robotic arms, the first joint angle, and the second joint angle, calculating the target joint angle of the target robotic arm, where the optimizer is used to solve the optimal solution of the joint angle of the target robotic arm according to the collected joint angles of multiple flexible robotic arms, and at least two of the acquisition times of the joint angles of multiple flexible robotic arms are different.

[0086] Based on the communication topology information among multiple flexible robotic arms, the first joint angles of other flexible robotic arms, and the second joint angles of the target robotic arm, the target joint angles of the target robotic arm can be calculated. Specifically, a preset optimizer can be used to solve for the target joint angles of the target robotic arm. Among them, the optimizer is used to solve for the optimal solution of the joint angles of the target robotic arm according to the collected joint angles of multiple flexible robotic arms, and the optimizer satisfies the aforementioned formula (2). That is to say, based on the communication topology information among multiple flexible robotic arms, the communication relationship among multiple flexible robotic arms can be determined, and thus . By substituting , the first joint angles, and the second joint angles into formula (2), and then solving formula (2) using the gradient descent method, for the target robotic arm i , the first variable constructed for the joint angles can converge to the optimal solution . In this way, the optimal solution can be used as the target joint angles of the target robotic arm.

[0087] The following will analyze why the first variable i constructed for the joint angles of the target robotic arm can converge to the optimal solution .

[0088] First of all, formula (2) can be expressed as the following formula (9):

[0089] (9);

[0090] Among them, represents the first variable constructed for the flexible robotic arm, , represents the intermediate variable constructed for the flexible robotic arm, , represents the function value of the local optimization function of, , represents the communication topology information among multiple flexible robotic arms, is the first variable collected at the sampling time , is the intermediate variable collected at the sampling time .

[0091] Then, let , , where represents the first optimization error, that is, the optimization error of, is the optimal solution of, represents the second optimization error, that is, The optimized error is the steady-state value. Then, Equation (10) can be obtained as follows:

[0092] (10);

[0093] where is the constructed second variable, is the constructed third variable, is the designed matrix. At this time, Equation (11) can be obtained as follows:

[0094]

[0095] (11);

[0096] where is the first orthogonal component of the first optimized error of is the second orthogonal component of the first optimized error of is the third orthogonal component of the second optimized error of is the fourth orthogonal component of the second optimized error of is the designed first matrix, is the designed second matrix. The first matrix and the second matrix are both constant matrices, represents the function value of the local optimization function of represents the function value of the local optimization function of the optimal solution of represents the communication topology information between multiple flexible robotic arms, is the sampling time at which the first orthogonal component is collected, is the sampling time at which the third orthogonal component is collected, is the sampling time at which the second orthogonal component is collected, is the sampling time at which the fourth orthogonal component is collected.

[0097] Subsequently, Lyapunov function (i.e., Lyapunov function) analysis can be carried out. At this time, the following Lyapunov function can be adopted: , where is the function value of the Lyapunov function, is the function value of the first sub-function, which is used for the second variable and the fourth orthogonal component are analyzed , is a design parameter, is the function value of the second sub-function, and is used to analyze the second orthogonal component and the fourth orthogonal component are analyzed, , is the function value of the third sub-function, and is used to analyze the time-delay term. The function value of the third sub-function satisfies the following formula (12):

[0098] (12);

[0099] wherein, is the variable of integration, and its integration interval is to , is the current moment, , represents the communication period, , represents the communication moment of other flexible robotic arms and the target robotic arm.

[0100] Then, the derivative of the function value of the first sub-function can be calculated as the following formula (13):

[0101] (13);

[0102] The derivative of the function value of the second sub-function can be calculated as the following formula (14):

[0103]

[0104] (14);

[0105] The derivative of the function value of the third sub-function can be calculated as the following formula (15):

[0106] (15);

[0107] Then, according to the triangle inequality, the following formula (16) can be obtained:

[0108] (16);

[0109] wherein, is the first difference, that is, the first orthogonal component at the current moment and the sampling moment The difference between the first orthogonal components, , is the second difference, that is, the difference between the second orthogonal component at the current moment and the second orthogonal component at the sampling moment . , is the third difference, that is, the difference between the third orthogonal component at the current moment and the third orthogonal component at the sampling moment . , is the fourth difference, that is, the difference between the fourth orthogonal component at the current moment and the fourth orthogonal component at the sampling moment . . Then the following formula (17) can be obtained:

[0110] ,

[0111] ,

[0112] (17);

[0113] wherein, , , , , , , , , , , , and are all normal constants set according to the actual situation, greater than 0.

[0114] Then, using the Jensen inequality, the formula (18) can be obtained:

[0115] (18);

[0116] Finally, according to the previous formula, the formula (19) can be obtained:

[0117] (19);

[0118] At this time, there exist a sufficiently large and , and a sufficiently small , such that , where is a set normal constant, so for the target robotic arm iThe first variable constructed based on the joint angles can converge to the optimal solution .

[0119] In some embodiments, controlling the electromagnetic torque of the target robotic arm according to the dynamic model of the target robotic arm, the state observables of the target robotic arm, and the target joint angles of the target robotic arm to adjust the joint angles of the target robotic arm to the target joint angles includes:

[0120] Determining a feedforward compensation amount of the target robotic arm according to the state observables of the target robotic arm and the target joint angles of the target robotic arm, where the feedforward compensation amount is used to offset control errors;

[0121] Controlling the electromagnetic torque of the target robotic arm according to the dynamic model of the target robotic arm, the feedforward compensation amount, and preset control parameters to adjust the joint angles of the target robotic arm to the target joint angles.

[0122] Through a preset controller, the electromagnetic torque of the target robotic arm can be controlled according to the dynamic model of the target robotic arm, the state observables of the target robotic arm, and the target joint angles of the target robotic arm. Among them, the controller is designed based on the fully actuated system model. Specifically, in the process of controlling the joint angles of the target robotic arm, first, through the controller, a feedforward compensation amount of the target robotic arm can be determined according to the state observables of the target robotic arm and the target joint angles of the target robotic arm. Among them, the feedforward compensation amount is used to offset control errors, that is, the non-linearity and uncertainty of the control process. The feedforward compensation amount is obtained according to the following formula (20):

[0123] (20);

[0124] Among them, is the feedforward compensation amount, is the function value of the basis function of the fuzzy logic algorithm obtained based on the joint angle observables , joint velocity observables , joint acceleration observables and joint jerk observables ; is the weight; is the fourth derivative of the first variable constructed for the joint angles of the target robotic arm i . When solving the feedforward compensation amount, the target joint angles can be substituted into this term; is the robust term used to compensate for the residual error .

[0125] Subsequently, the dynamic model of the target robotic arm can be processed and transformed according to the steps described above to obtain the processed dynamic model, i.e., Equation (8). Then, through the controller, based on the processed dynamic model, the state observation of the target robotic arm, and the target joint angle of the target robotic arm, the target electromagnetic torque of the target robotic arm is determined, and the motor of the target robotic arm is controlled according to the target electromagnetic torque to adjust the electromagnetic torque of the motor to the target electromagnetic torque, so as to adjust the joint angle of the target robotic arm to the target joint angle. Specifically, the target electromagnetic torque can be obtained according to the following Equation (21):

[0126] (21);

[0127] wherein, is the target electromagnetic torque of the target robotic arm, , is a preset control parameter, , determined according to the processed dynamic model, i.e., Equation (8).

[0128] In this way, through the foregoing steps, the joint angle of the target robotic arm can be adjusted to the target joint angle, so that the target robotic arm can complete specific actions during operation.

[0129] In some embodiments, the communication topology information includes the communication relationships between every two of the plurality of flexible robotic arms.

[0130] The communication topology information includes the communication relationships between every two of the plurality of flexible robotic arms. In one example, the plurality of flexible robotic arms include Flexible Robotic Arm 1, Flexible Robotic Arm 2, Flexible Robotic Arm 3, and Flexible Robotic Arm 4. If Flexible Robotic Arm 1, Flexible Robotic Arm 2, and Flexible Robotic Arm 3 communicate with each other, Flexible Robotic Arm 2 and Flexible Robotic Arm 4 communicate with each other, and Flexible Robotic Arm 4 does not communicate with Flexible Robotic Arm 1 and Flexible Robotic Arm 3, then the communication topology information including the foregoing communication relationships can be obtained, so as to control the plurality of flexible robotic arms based on the communication topology information.

[0131] Therefore, for the control method of the flexible robotic arm of the present application, an optimizer supporting asynchronous sampling is first designed. The communication cycle between every two flexible robotic arms in this optimizer can be different, which improves the accuracy of the subsequent control process and can convert the complex flexible robotic arm model into a relatively simple fully actuated system model, facilitating the subsequent controller design. Secondly, the fuzzy logic algorithm is used to approximate the non-linear and uncertain factors in the dynamic model, enabling the design of an observer. Thirdly, the designed observer includes adaptive parameters. The observer can obtain the state information of the flexible robotic arm based on the output signal and ensure the observation effect of the observer through the adaptive law of the adaptive parameters. Finally, the present application can design a controller with a simple form based on the fully actuated system model without using a complex backstepping design method, reducing the complexity of the controller.

[0132] Figure 2 It is a schematic structural diagram of the control device of the flexible robotic arm provided by an embodiment of the present application. As Figure 2 shown, an embodiment of the present application also provides a control device 200 of a flexible robotic arm, which can implement the above control method of the flexible robotic arm. The device 200 includes:

[0133] An acquisition module 201, configured to acquire the dynamic models of multiple flexible robotic arms;

[0134] An observation module 202, configured to, for a target robotic arm among the multiple flexible robotic arms, obtain a state observation quantity of the target robotic arm according to the dynamic model of the target robotic arm. The state observation quantity is used to describe the motion state of the target robotic arm, and the target robotic arm is any one of the multiple flexible robotic arms;

[0135] A calculation module 203, configured to calculate a target joint angle of the target robotic arm according to the communication topology information between the multiple flexible robotic arms, the first joint angles of other flexible robotic arms, and the second joint angle of the target robotic arm. The other flexible robotic arms are all the flexible robotic arms except the target robotic arm among the multiple flexible robotic arms. The first joint angle is the joint angle of the other flexible robotic arm at the communication moment between the other flexible robotic arm and the target robotic arm, and the second joint angle is the joint angle of the target robotic arm at the data sampling moment of the target robotic arm;

[0136] A control module 204, configured to control the electromagnetic torque of the target robotic arm according to the dynamic model of the target robotic arm, the state observation quantity of the target robotic arm, and the target joint angle of the target robotic arm, so as to adjust the joint angle of the target robotic arm to the target joint angle.

[0137] The specific implementation of the control device 200 of the flexible robotic arm is basically the same as the specific embodiments of the above-mentioned control method of the flexible robotic arm, and will not be elaborated here.

[0138] An embodiment of the present application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the above-mentioned control method of the flexible robotic arm. The electronic device can be any intelligent terminal including a tablet computer, an in-vehicle computer, etc.

[0139] Figure 3 It is a schematic diagram of the hardware structure of the electronic device provided by an embodiment of the present application. As Figure 3 shown, the electronic device includes:

[0140] A processor 301, which can be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided by the embodiments of the present application;

[0141] A memory 302, which can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 302 can store an operating system and other application programs. When implementing the technical solutions provided by the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 302, and the processor 301 is used to call and execute the control method of the flexible robotic arm in the embodiments of the present application;

[0142] An input / output interface 303, which is used to implement information input and output;

[0143] A communication interface 304, which is used to implement communication and interaction between this device and other devices, and can implement communication through a wired method (such as USB, network cable, etc.) or through a wireless method (such as mobile network, WIFI, Bluetooth, etc.);

[0144] A bus 305, which transmits information between various components of the device (such as the processor 301, the memory 302, the input / output interface 303, and the communication interface 304);

[0145] Among them, the processor 301, the memory 302, the input / output interface 303, and the communication interface 304 are communicatively connected to each other inside the device through the bus 305.

[0146] An embodiment of the present application also provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the above control method for the flexible robotic arm.

[0147] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory may include high-speed random access memory and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above networks include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0148] The control method, device, electronic device, and storage medium for the flexible robotic arm proposed in the present application obtain the dynamic models of multiple flexible robotic arms. For the target robotic arm among the multiple flexible robotic arms, according to the dynamic model of the target robotic arm, the state observation quantity of the target robotic arm is obtained. Then, according to the communication topology information between the multiple flexible robotic arms, the first joint angles of other flexible robotic arms, and the second joint angle of the target robotic arm, the target joint angle of the target robotic arm is calculated. Subsequently, according to the dynamic model of the target robotic arm, the state observation quantity of the target robotic arm, and the target joint angle of the target robotic arm, the electromagnetic torque of the target robotic arm is controlled to adjust the joint angle of the target robotic arm to the target joint angle. Through the above steps, the target joint angle of the target robotic arm can be determined according to the joint angles of other flexible robotic arms (collected at the communication moment between other flexible robotic arms and the target robotic arm) and the joint angle of the target robotic arm (collected at the data sampling moment of the target robotic arm). Furthermore, the joint angle of the target robotic arm can be controlled according to the target joint angle, which can be applied to the operation scenario of asynchronous sampling of multiple flexible robotic arms and improve the control precision in the control process of multiple flexible robotic arms.

[0149] The embodiments described in the embodiments of the present application are for more clearly illustrating the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art will know that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0150] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than those shown, or combine some steps, or different steps.

[0151] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0152] Those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, and their appropriate combinations.

[0153] As used in the specification of this application and the above drawings, the terms "first", "second", "third", "fourth", etc. (if any) are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of this application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily limit to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0154] It should be understood that in this application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects and indicates that three relationships can exist. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Among them, A and B can be singular or plural. The character " / " generally means that the associated objects before and after are in an "or" relationship. "At least one (one) of the following" or its similar expressions refer to any combination of these items, including any combination of single item (one) or plural items (ones). For example, at least one (one) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0155] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the above division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical or other forms.

[0156] The units described above as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0157] In addition, each functional unit in various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0158] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present application. And the aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store programs.

[0159] The preferred embodiments of the embodiments of the present application have been described above with reference to the accompanying drawings, and thus do not limit the scope of rights of the embodiments of the present application. Any modifications, equivalent replacements, and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of rights of the embodiments of the present application.

Claims

1. A control method for a flexible robotic arm, characterized in that, The method includes: Obtaining the dynamic models of multiple flexible robotic arms; For a target robotic arm among the multiple flexible robotic arms, based on the dynamic model of the target robotic arm, obtaining state observation quantities of the target robotic arm, where the state observation quantities are used to describe the motion state of the target robotic arm, and the target robotic arm is any one of the multiple flexible robotic arms; Based on the communication topology information between the multiple flexible robotic arms, the first joint angles of other flexible robotic arms, and the second joint angle of the target robotic arm, calculating the target joint angle of the target robotic arm, where the other flexible robotic arms are all the flexible robotic arms except the target robotic arm among the multiple flexible robotic arms, the first joint angle is the joint angle of the other flexible robotic arm at the communication moment between the other flexible robotic arm and the target robotic arm, and the second joint angle is the joint angle of the target robotic arm at the data sampling moment of the target robotic arm; Based on the dynamic model of the target robotic arm, the state observation quantities of the target robotic arm, and the target joint angle of the target robotic arm, controlling the electromagnetic torque of the target robotic arm to adjust the joint angle of the target robotic arm to the target joint angle.

2. The control method according to claim 1, characterized in that, Based on the communication topology information between the multiple flexible robotic arms, the first joint angles of other flexible robotic arms, and the second joint angle of the target robotic arm, calculating the target joint angle of the target robotic arm includes: Through a preset optimizer, based on the communication topology information between the multiple flexible robotic arms, the first joint angles, and the second joint angle, calculating the target joint angle of the target robotic arm, where the optimizer is used to solve the optimal solution of the joint angle of the target robotic arm according to the collected joint angles of the multiple flexible robotic arms, and at least two of the collection moments of the joint angles of the multiple flexible robotic arms are different.

3. The control method according to claim 1, wherein The controlling the electromagnetic torque of the target robotic arm based on the dynamic model of the target robotic arm, the state observation quantities of the target robotic arm, and the target joint angle of the target robotic arm to adjust the joint angle of the target robotic arm to the target joint angle includes: Based on the state observation quantities of the target robotic arm and the target joint angle of the target robotic arm, determining the feedforward compensation quantity of the target robotic arm, where the feedforward compensation quantity is used to offset the control error; Based on the dynamic model of the target robotic arm, the feedforward compensation quantity, and preset control parameters, controlling the electromagnetic torque of the target robotic arm to adjust the joint angle of the target robotic arm to the target joint angle.

4. The control method according to claim 1, wherein For a target robotic arm among the multiple flexible robotic arms, obtaining the state observation quantities of the target robotic arm based on the dynamic model of the target robotic arm includes: For a target robotic arm among the multiple flexible robotic arms, performing a conversion process on the dynamic model of the target robotic arm to obtain a processed dynamic model, where the conversion process is used to convert the dynamic model of the target robotic arm into the solvable processed dynamic model; Based on the processed dynamic model, preset observation parameters, and adaptive parameters, observe the motion state of the target robotic arm to obtain the state observation variables of the target robotic arm.

5. The control method according to claim 4, wherein The parameters of the dynamic model of the target robotic arm include the joint angles of the target robotic arm, the joint angular velocities of the target robotic arm, the motor angles of the target robotic arm, and the motor speeds of the target robotic arm. For the target robotic arm among multiple flexible robotic arms, performing a conversion process on the dynamic model of the target robotic arm to obtain a processed dynamic model includes: For the target robotic arm among multiple flexible robotic arms, using a plurality of first preset variables to perform a conversion process on the dynamic model of the target robotic arm to obtain the first dynamic model of the target robotic arm, and the plurality of first preset variables are constructed according to the joint angles of the target robotic arm, the joint angular velocities of the target robotic arm, the motor angles of the target robotic arm, and the motor speeds of the target robotic arm. Using a fuzzy logic algorithm to approximately process the state parameter function in the first dynamic model to obtain a second dynamic model, where the state parameter function is used to describe multiple state parameters of the target robotic arm, and the approximation process is used to convert the state parameter function into a solvable function approximated to the state parameter function. Substituting a second preset variable into the second dynamic model to obtain the processed dynamic model, and the second preset variable is constructed according to the first preset variable and the joint angles of the target robotic arm.

6. The control method according to claim 1, characterized in that The state observation variables include joint angle observation variables, joint velocity observation variables, joint acceleration observation variables, and joint jerk observation variables.

7. The control method according to claim 1, wherein The communication topology information includes the communication relationships between every two of the multiple flexible robotic arms.

8. A control device for a flexible robotic arm, characterized in that, The device includes: An acquisition module, configured to acquire the dynamic models of multiple flexible robotic arms. An observation module, configured to, for the target robotic arm among multiple flexible robotic arms, obtain the state observation variables of the target robotic arm according to the dynamic model of the target robotic arm, where the state observation variables are used to describe the motion state of the target robotic arm, and the target robotic arm is any one of the multiple flexible robotic arms. A calculation module, configured to calculate the target joint angle of the target robotic arm according to the communication topology information between multiple flexible robotic arms, the first joint angles of other flexible robotic arms, and the second joint angle of the target robotic arm, where the other flexible robotic arms are all flexible robotic arms except the target robotic arm among the multiple flexible robotic arms, the first joint angle is the joint angle of the other flexible robotic arm at the communication moment between the other flexible robotic arm and the target robotic arm, and the second joint angle is the joint angle of the target robotic arm at the data sampling moment of the target robotic arm. A control module, configured to control the electromagnetic torque of the target robotic arm according to the dynamic model of the target robotic arm, the state observation of the target robotic arm, and the target joint angles of the target robotic arm, so as to adjust the joint angles of the target robotic arm to the target joint angles.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the control method of the flexible robotic arm according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the control method of the flexible robotic arm according to any one of claims 1 to 7 is implemented.

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