A control method, system, device and medium for a multi-robot arm system

Abstract

The present invention discloses a control method, system, device and medium for a multi-robot-arm system, relating to the field of multi-robot-arm system control, including: within a non-linear system, using a composite disturbance observer for each robot arm to estimate the composite disturbance state of each robot arm; respectively using a local state observer and a distributed observer for each robot arm to obtain the local state observation error and the distributed observation error of each robot arm; determining the uniformly bounded state of each robot arm according to the local state observation error and the distributed observation error; and enabling the non-linear system to operate stably according to the composite disturbance state and the uniformly bounded state, so as to achieve the consistent tracking control of the non-linear system. The present invention can achieve precise control of the multi-robot-arm system.

Description

Technical Field

[0001] The present invention relates to the field of multi - robotic arm system control, and particularly to a control method, system, device and medium for a multi - robotic arm system. Background Art

[0002] With the rise of robot and artificial intelligence technologies, robotic arms play an important role in fields such as industrial production, modern medicine, and space exploration. In the field of multi - robotic arm system control, traditional control methods mainly include PID control, sliding - mode control, fuzzy - logic control, and model - predictive control - based methods.

[0003] In the prior art, in Method 1, sliding - mode control is adopted. Sliding - mode control is favored for its good robustness and tolerance to parameter uncertainties. It designs a sliding surface to make the system state quickly converge to the surface and slide along the surface to achieve the control purpose. In Method 2, a model - predictive - control - based scheme is adopted. It predicts the future state of the system according to the system model and optimizes the control sequence to minimize the cost function. It can handle constraint conditions and is easy to extend to multi - input multi - output systems.

[0004] However, in the control of multi - robotic arm systems, sliding - mode control and model - predictive - control - based methods lack anti - interference ability and cooperative operation ability, resulting in insufficient accuracy and inability to achieve precise control of multi - robotic arm systems. Summary of the Invention

[0005] Embodiments of the present invention provide a control method, system, device and medium for a multi - robotic arm system, which can solve the problem in the prior art that precise control of a multi - robotic arm system cannot be achieved.

[0006] Embodiments of the present invention provide a control method for a multi - robotic arm system, including the following steps: constructing a non - linear system in which one leader robotic arm leads multiple follower robotic arms to move through information interaction; within the non - linear system, using a composite disturbance observer for each robotic arm to estimate the composite disturbance state of each robotic arm; using a local state observer and a distributed observer for each robotic arm respectively to obtain the local state observation error and the distributed observation error of each robotic arm; determining the uniformly bounded state of each robotic arm according to the local state observation error and the distributed observation error; making the non - linear system operate stably according to the composite disturbance state and the uniformly bounded state, and being able to achieve the consensus tracking control of the non - linear system, where the consensus tracking control is used to represent that all robotic arms can achieve consensus motion.

[0007] Further, the specific steps of obtaining the local state observation error and the distributed observation error of each robotic arm include:

[0008] using a local state observer for each robotic arm

[0009]

[0010] Define the local state observation error as

[0011] where N represents the number of follower manipulators, i represents the i-th follower manipulator, is the estimate of the state x i , is the output of the state observer, u i ∈R p is the control input, K∈R n×q is the gain matrix such that A + KC is a Hurwitz matrix; θ i (t) represents the input time delay and satisfies θ i (t)=t - τ i (t), τ i (t) is a non-uniform time-varying input time delay; represents the uncertain external disturbance; and are the weight matrix and activation function of the radial basis neural network respectively, A∈R n×n , B∈R n×p , C∈R q×n are constant matrices;

[0012] Use a distributed observer for each manipulator

[0013]

[0014] where represents the estimate of the leader state, represents the output of the distributed observer, Λ∈R q×n is the gain matrix, e i is the consensus tracking error, and the formula is:

[0015]

[0016] Define the distributed observation error as

[0017] where x0 is the leader state, j represents the agent j index; N i represents the neighboring agents of agent i; a ij represents the communication weight between agent i and agent j; b i represents the communication weight between agent i and the leader; y0 represents the leader output.

[0018] Further, the specific steps for determining the uniformly bounded state of each robotic arm according to the local state observation error and the distributed observation error include:

[0019] Obtain the local state observation error and the distributed observation error

[0020] Derive from the two errors

[0021] When time t approaches infinity, obtain and Determine the uniformly bounded state of each robotic arm.

[0022] Further, the specific steps for achieving the consensus tracking control of the nonlinear system include:

[0023] Convert the consensus tracking control problem of the nonlinear system into an analysis of the stability of the nonlinear system through a composite disturbance observer, a local state observer, and a distributed observer, and obtain the stability model of the nonlinear system;

[0024] The stability model of the nonlinear system has the formula:

[0025]

[0026] Wherein, uncertain external disturbance; local state observation error; is the estimation error, and are respectively the radial basis neural network weight matrix and the activation function, represents the estimation of the leader state; A ∈ R n×n , B ∈ R n×p , C ∈ R q×n are constant matrices, K ∈ R n×q , Λ ∈ R q×n are gain matrices; e i is the consensus tracking error; u i ∈ R p is the control input; θ i represents the input time delay; error information, follower state estimation, represents the leader state estimation;

[0027] Construct a controller according to the stability model of the nonlinear system, and the controller can achieve the consensus tracking control model of the nonlinear system;

[0028] The controller has the formula:

[0029]

[0030] wherein, u i ∈R p is the control input; Γ i =-B T P i ,P i is a positive definite matrix; e i is the consensus tracking error; A ∈ R n×n is a constant matrix; is the input delay boundary which is a known constant; error information, follower state estimation, leader state estimation.

[0031] An embodiment of the present invention provides a control system for a multi - manipulator system, including:

[0032] A system construction module, configured to construct a non - linear system in which a leader manipulator leads multiple follower manipulators to move through information interaction; a control design module, configured to, within the non - linear system, estimate the composite disturbance state of each manipulator using a composite disturbance observer for each manipulator; use a local state observer and a distributed observer for each manipulator respectively to obtain the local state observation error and the distributed observation error of each manipulator; determine the uniformly bounded state of each manipulator according to the local state observation error and the distributed observation error; a control system construction module, configured to keep the non - linear system running stably according to the composite disturbance state and the uniformly bounded state, and be able to achieve the consensus tracking control of the non - linear system, where the consensus tracking control is used to represent that all manipulators can achieve consensus motion.

[0033] An embodiment of the present invention provides a computer device, including: a memory and a processor; the memory stores a computer program, and when the processor executes the computer program, it implements the control method of a multi - manipulator system described above.

[0034] An embodiment of the present invention provides a computer - readable storage medium, where the computer - readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the control method of a multi - manipulator system described above.

[0035] An embodiment of the present invention provides a control method, system, device and medium for a multi - manipulator system. Compared with the prior art, its beneficial effects are as follows:

[0036] The compound disturbance observer, the local state observer, and the distributed observer are collectively referred to as the adaptive observer. The adaptive observer can monitor and compensate in real time for the control error caused by external disturbances during the controlled operation of the robotic arm, avoiding drastic fluctuations in the control signal, and ultimately achieving precise control of the multi-robotic arm system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 Schematic diagram of the consensus tracking control scheme provided by an embodiment of the present invention;

[0038] Figure 2 Directed communication topology diagram provided by an embodiment of the present invention;

[0039] Figure 3 Schematic diagram of the local state observation error of the follower robotic arm provided by an embodiment of the present invention;

[0040] Figure 4 Schematic diagram of the local state observation error of the leader robotic arm provided by an embodiment of the present invention;

[0041] Figure 5 Schematic diagram of the system consensus tracking error provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be given with reference to the accompanying drawings. Many specific details are set forth in the following description in order to fully understand the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0043] Refer to Figures 1 to 5 , an embodiment of the present invention provides a control method for a multi-robotic arm system, including the following steps:

[0044] Step 1: Construct a nonlinear system in which one leader robotic arm leads multiple follower robotic arms to move through information interaction.

[0045] Step 2: In the nonlinear system, use a compound disturbance observer for each robotic arm to estimate the compound disturbance state of each robotic arm; use a local state observer and a distributed observer for each robotic arm respectively to obtain the local state observation error and the distributed observation error of each robotic arm; determine the uniformly bounded state of each robotic arm according to the local state observation error and the distributed observation error.

[0046] Step 3: According to the composite disturbance state and uniformly bounded state, keep the nonlinear system running stably, and achieve the consensus tracking control of the nonlinear system. The consensus tracking control is used to represent that all manipulators can achieve consensus motion.

[0047] The technical solution of the present invention focuses on a multi-manipulator tracking control method based on an adaptive observer, aiming to improve the tracking accuracy and robustness of the manipulator in a dynamic and uncertain environment.

[0048] The present invention aims to solve three key defects in the control field of multi-manipulator systems based on sliding mode control and model predictive control, specifically as follows:

[0049] 1. Eliminate the "chattering" effect and improve the control accuracy

[0050] In sliding mode control, the manipulator may experience "chattering", which is the vibration caused by the high-frequency switching of the control signal, affecting the accuracy of the manipulator and the reliability of long-term operation. By introducing an adaptive observer, the present invention can monitor and compensate the control error caused by parameter changes and external disturbances in real time, avoid the drastic fluctuation of the control signal, thus significantly reducing the "chattering" phenomenon and improving the tracking accuracy and stability of the manipulator.

[0051] 2. Enhance the cooperative operation ability and achieve smooth transition

[0052] In traditional sliding mode control and model predictive control, it is difficult to ensure the smoothness and coordination of the actions between manipulators during multi-manipulator cooperative operation, especially in tasks that require fine synchronization. By optimizing the control algorithm, the present invention considers the interaction forces and motion planning between manipulators, ensuring that multi-manipulators can achieve seamless cooperation when performing tasks, and can maintain a high degree of consistency even in a complex dynamic environment, improving the cooperative operation ability of the overall system.

[0053] 3. Improve the real-time performance and computational efficiency, and reduce the dependence on an accurate model

[0054] Although model predictive control can handle constraint conditions, it has a large amount of calculation and may not meet the requirements of real-time control, especially when dealing with large multi-manipulator systems. By designing an efficient adaptive observer and control strategy, the present invention not only reduces the demand for computing resources, improves the real-time response ability of the system, but also reduces the dependence on accurate model information. Even when there is a deviation between the model and the actual system, it can still maintain good control performance.

[0055] The detailed design scheme is as follows:

[0056] 1. Initialization stage

[0057] Set up the dynamic model of the robotic arm, including parameters such as mass, inertia, and friction coefficient.

[0058]

[0059] Among them, M i is the moment of inertia, q i is the angular position, the relative velocity deflection angle, the angular acceleration, m i is the mass of the connecting rod, g = 9.8 m / s 2 is a positive constant, l i is the length of the robotic arm, ζ i is the control torque, θ i represents the input time delay, ò i is the disturbance acting on the robotic arm.

[0060] Define x i1 = q i , u i = ζ i (θ i ). The nonlinear system consists of N + 1 robotic arms, where N are follower robotic arms and 1 is the leader robotic arm. The leader index is 0, and the follower indices range from 1 to N. Then the system can be regarded as the following system:

[0061]

[0062] Among them, x i ∈R n , u i ∈R p , y i ∈R q are the system state, control input, and output respectively. The state variables are not measurable, only the output can be obtained through measurement, and u0(θ0(t)) = 0. θ i (t) is a smooth function and satisfies θ i (t) = t - τ i (t), τ i (t) is a non-uniform time-varying input time delay. d i (t): R + →R n represents the uncertain external disturbance. A ∈ R n×n , B ∈ R n×p , C ∈ R q×n are constant matrices, (A, C) is observable, and (A, B) is controllable. f i : R n →R n is an uncertain smooth nonlinear vector function.

[0063] Definition Among them, is the estimated value of x i and will be designed and implemented in the state observer. With the help of radial basis function neural networks (RBFNNs), the uncertain nonlinear function is approximated It can be obtained that:

[0064]

[0065] Among them, is regarded as the composite disturbance. Among them and are positive constants. The system model (2) can be rewritten as:

[0066]

[0067] 2. State Observation and Parameter Estimation

[0068] Construct a consensus control strategy for each manipulator. This scheme consists of three observers based on adaptive RBFNNs, namely the composite disturbance observer local state observer and distributed observer

[0069] (1) Design a composite disturbance observer to estimate the composite disturbance and define the following auxiliary variables:

[0070]

[0071] Among them, ρ i is a positive constant.

[0072] According to formulas (4) and (5), it can be obtained that:

[0073]

[0074] Next, define the estimate of the composite disturbance:

[0075]

[0076] Among them, is the estimate of the composite disturbance is the estimate of the auxiliary variable is the estimate of the optimal weight matrix W i * * * *

[0077] Define the estimation error variable Combined with equation (7), we can obtain:

[0078]

[0079] where,

[0080] For Taking the derivative and combining with (6) and (7), we can obtain:

[0081]

[0082] where, is the weight matrix W i * 's estimation error.

[0083] (2) Design a local state observer for each follower manipulator as follows:

[0084]

[0085] where, is the estimation of state x i and is the output of the state observer, K ∈ R n×q is the gain matrix such that A + KC is a Hurwitz matrix.

[0086] Define the local state observation error as We can obtain:

[0087]

[0088] where,

[0089] Positive design parameters γ, satisfy the following inequality group:

[0090]

[0091] The adaptive law of the weight matrix W * is:

[0092]

[0093] where, μ i is the design parameter.

[0094] And it satisfies where, λ1 is 's minimum eigenvalue, λ2 is 's minimum value, λ3 is 's minimum value.

[0095] (3) Design the following output-feedback-based distributed observer for each follower,

[0096]

[0097] where, represents the estimate of the leader state, represents the output of the distributed observer, Λ ∈ R q×n is the gain matrix, e i is the consensus tracking error, and its specific form is:

[0098]

[0099] where, x0 is the leader state, j represents the index of agent j; N i represents the neighboring agents of agent i; a ij represents the communication weight between agent i and agent j; b i represents the communication weight between agent i and the leader; y0 represents the leader output. A robotic arm is an agent.

[0100] Define the distributed observation error as For take the derivative, and we can get:

[0101]

[0102] If the following inequality is satisfied, the state of the leader can be asymptotically estimated by the distributed observer (15), that is where, is the upper bound.

[0103]

[0104] where, is a positive definite matrix, and γ is a positive constant. can achieve semi-global ultimate uniform boundedness, that is λ z is the minimum eigenvalue of (Q z -γ||P z ||).

[0105] 3. Controller Design

[0106] According to the definition and we can get Based on the above analysis, we have and is ultimately uniformly bounded, that is, when t → ∞, we have Thus, we can know that if is ultimately uniformly bounded. Through two types of observers (7) and (11), the nonlinear system (2) can achieve consensus tracking control.

[0107] Define The consensus tracking control problem of the nonlinear system (2) is transformed into the stability analysis of the following nonlinear system

[0108]

[0109] where

[0110] The controller is designed as follows:

[0111]

[0112] where, Γ i =-B T P i , can achieve consensus tracking control. At the same time, the following conditions are satisfied:

[0113]

[0114] where, P i is a positive definite matrix, Y i =P i -1 ,

[0115] In the control method, the practical information is as follows:

[0116] 1. Parameter selection of the adaptive observer: Select the parameters θ i =1, γ i =0.04, β i =4, ι1 = 1.25, ι2 = 1.05. According to B and β i , select the matrix S i =[1.4869 -4.1721]. Let the parameter K = [1,2] T , Λ = [1,2] T , γ = 0.4,

[0117] The initial state is x0(0)=[0.4,0.2] T , x1(0)=[1.2,2.3] T , x2(0)=[-2.1,1.3] T , x3(0)=[1.2,-2.5] T , x4(0)=[2.1,1.3] T ,

[0118] A radial basis neural network with a hidden layer containing 9 neurons is adopted. Denote the basis function vector, where The width μ of the activation function i = 1, and the center c i is uniformly distributed within the interval [-2, 2]×[-2, 2].

[0119] Selecting appropriate initial estimates is crucial for the convergence speed and accuracy of the observer. Experimental data shows that the initial estimates should be close to 50% - 80% of the true value, and the learning rate should be between 0.01 and 0.1 to balance the requirements of fast response and stability.

[0120] 2. Optimization of the control law design: The design of the control law should take into account the physical limitations of the robotic arm, such as maximum torque and acceleration. Select x i = [x i1 , x i2 T ∈R 2 , f i (x i ) = [0, -m i gl i sin(x i1 ) / 2M i T , d i (t) = [0, ò i / M i T , C = [1, 0], B = [0, 1 / M i T . Design parameters l i = 0.2m, m i = 0.3kg, M i = 1kg.m, θ i = t - (0.01(t + 1) / t + 1), ò i = 0.26sin(x i1 ), ò0 = 0, u0(θ0) = 0. By introducing soft boundaries and penalty terms, it is possible to avoid the control signal exceeding the safe range while ensuring that the robotic arm can track the target trajectory quickly and smoothly.

[0121] 3. Strategies for cooperative control: In a multi-robotic arm system, using concepts from graph theory and network theory to define the connection relationships and information flows between robotic arms helps to optimize the cooperative control strategies. In Figure 2 the directed communication topology shown, the application example consists of 5 manipulators, indexed 0 - 4.

[0122] Among them, it can be obtained that B = [1 0 0 1],

[0123] 4. Experimental verification: Build a simulation model of the multi-manipulator system on the MATLAB / Simulink platform, including tracking accuracy, response time, and computational load.

[0124] Through the above improvements, the present invention provides a more robust, accurate, and efficient multi-manipulator tracking control method, which is particularly suitable for industrial automation, aerospace, medical surgery, and other fields that require high precision and real-time response, bringing significant progress to the control technology of multi-manipulator systems.

[0125] The beneficial effects of the present invention are as follows:

[0126] 1. Significantly improve tracking accuracy: Through the dynamic parameter estimation of the adaptive observer, the present invention can compensate in real time for the tracking errors caused by parameter uncertainties (such as the inertia of the manipulator and the change of the friction coefficient) and external disturbances (such as wind force and vibration), enabling the manipulator to follow the predetermined trajectory more accurately and improving the operation accuracy.

[0127] 2. Enhance robustness and adaptability: The introduction of the adaptive observer enhances the robustness of the control system, enabling it to maintain a stable performance in the face of various unknown or changing environmental conditions. This is crucial for applications in industrial automation, aerospace, medical surgery, and other fields, as these scenarios often involve a large number of unpredictable factors.

[0128] 3. Optimize the collaborative operation ability: The present invention specifically designs a collaborative control strategy, which can enable multiple manipulators to maintain coordinated actions when performing complex tasks, reduce the risk of collision, and improve the overall operation efficiency and safety of the multi-manipulator system.

[0129] 4. Improve real-time performance and computational efficiency: Compared with model predictive control, the method of the present invention reduces the burden of real-time calculation while ensuring the control effect, improving the response speed of the system, which is particularly important for scenarios that require rapid response.

[0130] 5. Economic benefits: The implementation of the present invention can significantly improve production efficiency, reduce the scrap rate, and lower the maintenance cost. In the field of medical surgical robots, higher precision means less trauma and faster recovery, bringing a better treatment experience to patients and also reducing medical costs.

[0131] The embodiment of the present invention provides a control system for a multi-manipulator system, including:

[0132] A system construction module is used to construct a nonlinear system in which a leader robotic arm leads multiple follower robotic arms to move through information interaction. A control design module is used to, within the nonlinear system, estimate the composite disturbance state of each robotic arm using a composite disturbance observer for each robotic arm; obtain the local state observation error and the distributed observation error of each robotic arm respectively using a local state observer and a distributed observer for each robotic arm; and determine the uniformly bounded state of each robotic arm according to the local state observation error and the distributed observation error. A control system construction module is used to keep the nonlinear system running stably according to the composite disturbance state and the uniformly bounded state, and can achieve the consensus tracking control of the nonlinear system, and the consensus tracking control is used to represent that all robotic arms can achieve consensus motion.

[0133] An embodiment of the present invention provides a computer device, including: a memory and a processor; the memory stores a computer program, and when the processor executes the computer program, the steps of a control method for a multi-robotic arm system are implemented.

[0134] An embodiment of the present invention provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of a control method for a multi-robotic arm system are implemented.

[0135] A specific embodiment is as follows:

[0136] 1. Dynamic parameter adaptive mechanism: By updating the estimation of the dynamic parameters of the robotic arm in real time, the present invention can automatically adapt to changes in the working environment, reduce the dependence on external calibration, and lower the maintenance cost.

[0137] 2. Cooperative control optimization algorithm: The cooperative control strategy of the present invention uses advanced optimization algorithms to intelligently adjust the relative positions and motions between robotic arms, ensuring the high efficiency and safety of the multi-robotic arm system during task execution, and opening up a new way for the automated execution of complex tasks.

[0138] 3. Economic benefit analysis: In the field of industrial manufacturing, higher precision and efficiency mean increased output and reduced costs of the production line. In the medical field, the application of the present invention can reduce the operation time and complications, improve patient satisfaction, save costs for medical institutions, and also improve the quality and level of medical services. These factors jointly promote the dual improvement of the economic and social benefits of related industries.

[0139] The above-described embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the appended claims.

Claims

1. A control method for a multi-manipulator system, characterized in that: The following steps are involved: Construct a nonlinear system in which a leader robot leads multiple follower robots to move through information interaction; In the nonlinear system, a composite disturbance observer is used for each manipulator to estimate the composite disturbance state of each manipulator; a local state observer and a distributed observer are used for each manipulator to obtain the local state observation error and the distributed observation error of each manipulator; the consistently bounded state of each manipulator is determined based on the local state observation error and the distributed observation error; The nonlinear system is kept running stably according to the composite disturbance state and the consistent bounded state, and the consistent tracking control of the nonlinear system can be realized. The consistent tracking control is used to indicate that all the robotic arms can realize consistent motion.

2. A control method for a multi-robot system as claimed in claim 1, characterized in that: The specific steps of obtaining the local state observation error and the distributed observation error of each robotic arm include: Use a local state observer for each robot The local state observation error is defined as Where N represents the number of follower robotic arms, i represents the i-th follower robotic arm, is the state x i The estimate, is the output of the state observer, u i ∈R p is the control input, K∈R n×q is the gain matrix, making A+KC a Hurwitz matrix; θ i (t) represents the input delay and satisfies θ i (t) = t - τ i (t), τ i (t) is the non-uniform time-varying input delay; represents uncertain external disturbance; and are the weight matrix and activation function of the radial basis neural network, A∈R n ×n , B∈R n×p , C∈R q×n is a constant matrix; Use a distributed observer for each robot in, represents an estimate of the leader's state, represents the output of the distributed observer, Λ∈R q×n is the gain matrix, e i is the consistent tracking error, and the formula is: The distributed observation error is defined as Among them, x0 is the leader state, j represents the index of agent j; N i represents the neighboring agent of agent i; a ij represents the communication weight between agent i and agent j; b i represents the communication weight between agent i and the leader; y0 represents the leader output.

3. A control method for a multi-robot system as claimed in claim 2, characterized in that: The steps of determining the consistent bounded state of each robot arm according to the local state observation error and the distributed observation error include: Get local state observation error and distributed observation errors According to the two errors When time t tends to infinity, we get and Determine the consistent and bounded state of each robot.

4. The control method of a multi-robot system according to claim 1, characterized in that: The specific steps of realizing the consistency tracking control of the nonlinear system include: The consistency tracking control problem of nonlinear system is transformed into the stability analysis of nonlinear system through composite disturbance observer, local state observer and distributed observer, and the stability model of nonlinear system is obtained. The stability model of the nonlinear system is as follows: in, Uncertain external disturbances; Local state observation error; is the estimation error, and They are the weight matrix and activation function of the radial basis neural network, Represents the estimate of the leader state; A∈R n×n , B∈R n×p , C∈R q×n is a constant matrix, K∈R n×q ,Λ∈R q×n is the gain matrix; e i is the consistent tracking error; u i ∈R p is the control input; θ i Indicates input delay; Error information, Follower state estimation, represents the leader state estimate; Building a controller according to the stability model of the nonlinear system, wherein the controller can realize the consistency tracking control of the nonlinear system; The controller formula is: Among them, u i ∈R p is the control input; Γ i =-B T P i , P i is a positive definite matrix; is the input delay bound which is a known constant.

5. A control system for a multi-manipulator system, characterized in that: include: System building module, used to build a nonlinear system in which a leader robot leads multiple follower robots to move through information interaction; A control design module is used to estimate the composite disturbance state of each manipulator using a composite disturbance observer for each manipulator in a nonlinear system; obtain the local state observation error and the distributed observation error of each manipulator using a local state observer and a distributed observation error for each manipulator respectively; and determine the consistent bounded state of each manipulator according to the local state observation error and the distributed observation error; The control system building module is used to keep the nonlinear system running stably according to the composite disturbance state and the consistent bounded state, and can realize the consistent tracking control of the nonlinear system, wherein the consistent tracking control is used to indicate that all the robotic arms can realize consistent motion.

6. A computer device comprising: Memory and processor; The memory stores a computer program, wherein the processor implements a control method for a multi-robotic arm system according to any one of claims 1 to 4 when executing the computer program.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, a control method for a multi-manipulator system according to any one of claims 1 to 4 is implemented.

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