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CN119141542A - Control method, system, equipment and medium for multi-mechanical arm system - Google Patents

Control method, system, equipment and medium for multi-mechanical arm system Download PDF

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Publication number
CN119141542A
CN119141542A CN202411437109.1A CN202411437109A CN119141542A CN 119141542 A CN119141542 A CN 119141542A CN 202411437109 A CN202411437109 A CN 202411437109A CN 119141542 A CN119141542 A CN 119141542A
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state
manipulator
observer
control
observation error
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CN119141542B (en
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赵秀娟
谢孔昊
郎长胜
杨凡
万博洋
张正
唐靖
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Jiangxi Science and Technology Normal University
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1602—Programme controls characterised by the control system, structure, architecture
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1679—Programme controls characterised by the tasks executed
    • B25J9/1682—Dual arm manipulator; Coordination of several manipulators
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

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  • Engineering & Computer Science (AREA)
  • Robotics (AREA)
  • Mechanical Engineering (AREA)
  • Automation & Control Theory (AREA)
  • Feedback Control In General (AREA)

Abstract

本发明公开了一种多机械臂系统的控制方法、系统、设备及介质，涉及多机械臂系统控制领域，包括：在非线性系统内，对每个机械臂使用复合扰动观测器估计每个机械臂的复合扰动状态；对每个机械臂分别使用局部状态观测器和分布式观测器，获得每个机械臂的局部状态观测误差和分布式观测误差；根据局部状态观测误差和分布式观测误差确定每个机械臂一致有界状态；根据复合扰动状态和一致有界状态让非线性系统保持稳定运行，能够实现非线性系统的一致性跟踪控制。本发明能够实现多机械臂系统的精准控制。

The present invention discloses a control method, system, device and medium for a multi-manipulator system, and relates to the field of multi-manipulator system control, including: in a nonlinear system, using a composite disturbance observer for each manipulator to estimate the composite disturbance state of each manipulator; using a local state observer and a distributed observer for each manipulator to obtain the local state observation error and the distributed observation error of each manipulator; determining the consistent bounded state of each manipulator according to the local state observation error and the distributed observation error; keeping the nonlinear system running stably according to the composite disturbance state and the consistent bounded state, and realizing the consistent tracking control of the nonlinear system. The present invention can realize the precise control of the multi-manipulator system.

Description

Control method, system, equipment and medium for multi-mechanical arm system
Technical Field
The present invention relates to the field of control of multiple mechanical arm systems, and in particular, to a method, a system, an apparatus, and a medium for controlling a multiple mechanical arm system.
Background
With the rise of robots and artificial intelligence technologies, mechanical arms play an important role in the fields of industrial production, modern medical treatment, space exploration and the like. In the field of multi-mechanical arm system control, the traditional control method mainly comprises PID control, sliding mode control, fuzzy logic control and model-based prediction control.
In the prior art, the sliding mode control is adopted in the first mode, and is favored for good robustness and tolerance to parameter uncertainty, and the sliding mode surface is designed to enable the system state to quickly converge on the surface and slide along the surface, so that the control purpose is achieved. The second mode adopts a scheme based on model predictive control, predicts future states according to a system model and optimizes a control sequence to minimize a cost function, can process constraint conditions, and is easy to expand to a multiple-input multiple-output system.
However, the sliding mode control and the model prediction-based control method lack of anti-interference capability and collaborative operation capability in the control of the multi-mechanical arm system, so that the precision is insufficient, and the precise control of the multi-mechanical arm system cannot be realized.
Disclosure of Invention
The embodiment of the invention provides a control method, a system, equipment and a medium for a multi-mechanical arm system, which can solve the problem that the precise control of the multi-mechanical arm system cannot be realized in the prior art.
The embodiment of the invention provides a control method of a multi-mechanical arm system, which comprises the steps of constructing a nonlinear system in which a leader mechanical arm leads a plurality of follower mechanical arms to move in an information interaction mode, estimating a composite disturbance state of each mechanical arm by using a composite disturbance observer for each mechanical arm in the nonlinear system, respectively using a local state observer and a distributed observer for each mechanical arm to obtain a local state observation error and a distributed observation error of each mechanical arm, determining a consistent and limited state of each mechanical arm according to the local state observation error and the distributed observation error, enabling the nonlinear system to keep stable operation according to the composite disturbance state and the consistent and limited state, and realizing consistent tracking control of the nonlinear system, wherein the consistent tracking control is used for indicating that all the mechanical arms can realize consistent movement.
Further, the obtaining the local state observation error and the distributed observation error of each mechanical arm specifically includes:
Using local state observers for each arm
Defining local state observation errors as
Wherein N represents the number of the follower mechanical arms, i represents the ith follower mechanical arm,Is an estimate of the state x i,Is the output of the state observer, u i∈Rp is the control input, K ε R n×q is the gain matrix such that A+KC is the Hurwitz matrix of Hurwitz, θ i (t) represents the input delay and satisfies that θ i(t)＝t-τi(t),τi (t) is a non-uniform time-varying input delay; representing an uncertain external disturbance; And The method is characterized in that the method comprises the steps of respectively obtaining a radial basis function neural network weight matrix and an activation function, wherein A epsilon R n×n,B∈Rn×p,C∈Rq×n is a constant matrix;
Using a distributed observer for each robotic arm
Wherein, Representing an estimate of the status of the leader,Representing the output of the distributed observer, Λ e R q×n is the gain matrix, e i is the consistent tracking error, and the formula is:
Defining a distributed observation error as
Where x 0 is the leader state, j represents the index of agent j, N i represents the neighbor 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, and y 0 represents the leader output.
Further, the determining the consistent bounded state of each mechanical arm according to the local state observation error and the distributed observation error specifically includes the following steps:
Obtaining local state observation errors Distributed observation errors
Derived from two errors
When the time t tends to be infinite, it is derived thatAnd is also provided withA consistent bounded state for each robotic arm is determined.
Further, the specific steps of implementing the consistent tracking control of the nonlinear system include:
the method comprises the steps of converting a consistency tracking control problem of a nonlinear system into a stability of the nonlinear system through a composite disturbance observer, a local state observer and a distributed observer, and obtaining a stability model of the nonlinear system;
the stability model of the nonlinear system is represented by the formula:
Wherein, Uncertainty of external disturbance; Local state observation errors; Is the error of the estimation and, AndThe radial basis function neural network weight matrix and the activation function,Representing an estimate of the leader state, A ε R n×n,B∈Rn×p,C∈Rq×n is a constant matrix, K ε R n×q,Λ∈Rq×n is a gain matrix, e i is a consistent tracking error, u i∈Rp is a control input, θ i represents an input delay; the error information is used to determine the error information, The state of the follower state estimate,Representing a leader state estimate;
Constructing a controller according to a stability model of the nonlinear system, wherein the controller can realize a consistency tracking control model of the nonlinear system;
the controller has the formula:
Wherein u i∈Rp is the control input, Γ i＝-BTPi,Pi is the positive definite matrix, e i is the coherence tracking error, A εR n×n is the constant matrix; Is that the input delay boundary is a known constant; the error information is used to determine the error information, The state of the follower is estimated and,And (5) leader state estimation.
The embodiment of the invention provides a control system of a multi-mechanical arm system, which comprises the following components:
The system comprises a system construction module, a control design module and a control system construction module, wherein the system construction module is used for constructing a nonlinear system in which a leader mechanical arm leads a plurality of follower mechanical arms to move in an information interaction mode, the control design module is used for estimating a composite disturbance state of each mechanical arm by using a composite disturbance observer for each mechanical arm in the nonlinear system, a local state observer and a distributed observer are respectively used for each mechanical arm to obtain a local state observation error and a distributed observation error of each mechanical arm, a consistent bounded state of each mechanical arm is determined according to the local state observation error and the distributed observation error, the control system construction module is used for enabling the nonlinear system to keep stable running according to the composite disturbance state and the consistent bounded state, and consistent tracking control of the nonlinear system can be achieved, and the consistent tracking control is used for indicating that all mechanical arms can achieve consistent movement.
The embodiment of the invention provides a computer device which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the control method of the multi-mechanical arm system when executing the computer program.
An embodiment of the present invention provides a computer readable storage medium storing a computer program, where the computer program when executed by a processor implements a method for controlling a multi-mechanical arm system as described above.
The embodiment of the invention provides a control method, a system, equipment and a medium of a multi-mechanical arm system, which have the following beneficial effects compared with the prior art:
The composite disturbance observer, the local state observer and the distributed observer are collectively called as a self-adaptive observer, the self-adaptive observer can monitor and compensate control errors caused by external interference of the mechanical arm in the process of controlled operation in real time, severe fluctuation of control signals is avoided, and finally accurate control of the multi-mechanical arm system is realized.
Drawings
FIG. 1 is a schematic diagram of a consistent tracking control scheme provided by an embodiment of the present invention;
FIG. 2 is a directed communication topology provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram of a local state observation error of a follower mechanical arm according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a local state observation error of a leader manipulator according to an embodiment of the present invention;
Fig. 5 is a schematic diagram of a system consistent tracking error provided by an embodiment of the present invention.
Detailed Description
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to the appended drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. The present invention may be embodied in many other forms than described herein and similarly modified by those skilled in the art without departing from the spirit of the invention, whereby the invention is not limited to the specific embodiments disclosed below.
Referring to fig. 1 to 5, an embodiment of the present invention provides a control method of a multi-mechanical arm system, including the following steps:
step one, constructing a nonlinear system in which a leader mechanical arm leads a plurality of follower mechanical arms to move in an information interaction mode.
In a nonlinear system, estimating a composite disturbance state of each mechanical arm by using a composite disturbance observer, respectively using a local state observer and a distributed observer for each mechanical arm to obtain a local state observation error and a distributed observation error of each mechanical arm, and determining a consistent bounded state of each mechanical arm according to the local state observation error and the distributed observation error.
And thirdly, enabling the nonlinear system to keep stable running according to the composite disturbance state and the consistent bounded state, and realizing consistent tracking control of the nonlinear system, wherein the consistent tracking control is used for indicating that all mechanical arms can realize consistent movement.
The technical scheme of the invention is developed around a multi-mechanical arm tracking control method based on a self-adaptive observer, and aims to improve the tracking precision and robustness of the mechanical arm under dynamic and uncertain environments.
The invention aims to solve three key defects of a sliding mode control-based and model prediction control scheme in the field of multi-mechanical arm system control, and specifically comprises the following steps:
1. Eliminating buffeting effect and raising control precision
In slip mode control, the robot arm may experience "buffeting" due to vibrations caused by high frequency switching of control signals, affecting the accuracy of the robot arm and the reliability of long-term operation. The invention can monitor and compensate control errors caused by parameter changes and external interference in real time by introducing the self-adaptive observer, and avoid severe fluctuation of control signals, thereby obviously reducing the buffeting phenomenon and improving the tracking precision and stability of the mechanical arm.
2. Enhancing collaborative operation capability and realizing smooth transition
When the traditional sliding mode control and model prediction control are used for collaborative operation of a plurality of mechanical arms, smoothness and coordination of actions among the mechanical arms are difficult to ensure, and particularly in tasks needing fine synchronization. According to the invention, through optimizing the control algorithm, the interaction force and motion planning among the mechanical arms are considered, so that seamless cooperation of multiple mechanical arms can be realized when tasks are executed, high consistency can be maintained even under a complex dynamic environment, and the cooperative operation capability of the whole system is improved.
3. The real-time performance and the calculation efficiency are improved, and the dependence on an accurate model is reduced
Model predictive control, although capable of handling constraints, is computationally intensive and may not meet real-time control requirements, particularly when handling large multi-robot systems. According to the invention, by designing the efficient self-adaptive observer and the control strategy, the demand of computing resources is reduced, the real-time response capability of the system is improved, the dependence on accurate model information is reduced, and good control performance can be maintained even if the model deviates from an actual system.
The detailed design scheme is as follows:
1. initialization phase
And setting a mechanical arm dynamics model which comprises parameters such as mass, inertia, friction coefficient and the like.
Where M i is the moment of inertia, q i is the angular position,The relative velocity is offset by an angle of incidence,Angular acceleration, mass of m i link, g=9.8 m/s 2 is normal number, length of l i robot arm, ζ i is control moment, θ i represents input delay, co i is disturbance acting on the robot arm.
The definition of x i1＝qi is that,U i＝ζi(θi), a nonlinear system consisting of n+1 robotic arms, where N follower robotic arms and 1 leader robotic arm. The leader index number is 0 and the follower index number is from 1 to N. The system can be regarded as the following system:
Wherein x i∈Rn,ui∈Rp,yi∈Rq is the system state, control input, output, respectively. The state variables are not measurable, only the output can be obtained by measurement, and u 0(θ0(t))＝0.θi (t) is a smoothing function and satisfying θ i(t)＝t-τi(t),τi (t) is a non-uniform time-varying input delay. d i(t):R+→Rn denotes an uncertain external disturbance. A εR n×n,B∈Rn×p,C∈Rq×n is a constant matrix, (A, C) is observable and (A, B) is controllable. f i:Rn→Rn is an uncertainty smooth nonlinear vector function.
Definition of the definitionWherein, Is an estimate of x i and is to be designed and implemented in a state observer, a non-deterministic nonlinear function by means of a radial basis neural network (RBFNNs)Performing approximationThe method can obtain:
Wherein, Seen as a composite disturbance. Wherein the method comprises the steps of AndIs a positive constant. The system model (2) can be rewritten as:
2. State observation and parameter estimation
A consistency control strategy is constructed for each mechanical arm, and the scheme consists of three observers based on self-adaption RBFNNs, namely a composite disturbance observerLocal state observerDistributed observer
(1) Designing a composite disturbance observer to estimate composite disturbance, and defining the following auxiliary variables:
Where ρ i is a positive constant.
From formulas (4) and (5), it is possible to obtain:
Next, an estimate of the composite disturbance is defined:
Wherein, Is a composite disturbanceIs used for the estimation of (a),Is an auxiliary variableIs used for the estimation of (a),Is an estimate of the optimal weight matrix W i *.
Defining an estimation error variableThe combination (7) can be obtained by:
Wherein,
For a pair ofDeriving and combining (6) and (7), obtaining:
Wherein, Is the estimated error of the weight matrix W i *.
(2) Designing a local state observer for each follower armThe following is shown:
Wherein, Is an estimate of the state x i,Is the output of the state observer, K ε R n×q is the gain matrix, so that A+KC is the Hurwitz matrix.
Defining local state observation errors asThe method can obtain:
Wherein,
Positive design parametersγ,The following set of inequalities is satisfied:
The adaptive rule of the weight matrix W * is:
Wherein μ i is a design parameter.
And meet the following requirementsWherein lambda 1 isIs lambda 2 isIs a minimum value of lambda 3 Is a minimum of (2).
(3) A distributed observer based on output feedback is designed for each follower as follows,
Wherein, Representing an estimate of the status of the leader,Representing the output of the distributed observer, Λ e R q×n is the gain matrix, e i is the coherence tracking error, in the specific form:
Where x 0 is the leader state, j represents the index of agent j, N i represents the neighbor 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, and y 0 represents the leader output. One mechanical arm is an intelligent body.
Defining a distributed observation error asFor a pair ofDerivative is obtained by:
the state of the leader can be estimated asymptotically by the distributed observer (15), if the following inequality is satisfied, i.e Wherein, Is the upper boundary.
Wherein, Is a positive definite matrix and γ is a positive constant.Can reach the semi-global final consistency and limitation, namelyLambda z is the minimum eigenvalue of (Q z-γ||Pz |).
3. Controller design
According to the definitionAndIs available in the form ofBased on the analysis, there areAndIs finally consistent and bounded, i.e. when t → infinity, there isThus, it can be seen that ifIs ultimately coherent bounded, and a nonlinear system (2) can achieve coherent tracking control through both types of observers (7) and (11).
Definition of the definitionThe problem of consistency tracking control of the nonlinear system (2) is converted into stability analysis of the nonlinear system,
Wherein,
The controller is designed as follows:
wherein Γ i＝-BTPi, a consistency tracking control may be implemented. The following conditions are satisfied at the same time:
where P i is a positive definite matrix, Y i＝Pi -1,
In the control scheme, information put into practical use is as follows:
1. Parameter selection of adaptive observer, selection of parameters Θ i＝1,γi＝0.04,βi＝4,ι1＝1.25,ι2 =1.05. Based on B and beta i, a matrix is selectedS i = [ 1.4869-4.1721 ]. Let the parameter k= [1,2] T,Λ＝[1,2]T, γ=0.4,
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,
A radial basis neural network with a hidden layer containing 9 neurons is employed,Representing a basis function vector in whichThe activation function width mu i =1, the center c i being uniformly distributed over the interval [ -2,2] × [ -2, 2].
The selection of an appropriate initial estimate is critical to the convergence speed and accuracy of the observer. Experimental data show that the initial estimated value is close to 50% -80% of the true value, and the learning rate is between 0.01 and 0.1 so as to balance the requirements of quick response and stability.
2. Optimization of control law design the design of the control law should take into account physical constraints of the robotic arm, such as maximum torque and acceleration. Selection of xi＝[xi1,xi2]T∈R2,fi(xi)＝[0,-miglisin(xi1)/2Mi]T,di(t)＝[0,òi/Mi]T,C= [1,0], b= [0,1/M i]T. Design parameters li＝0.2m,mi＝0.3kg,Mi＝1kg.m,θi＝t-(0.01(t+1)/t+1),òi＝0.26sin(xi1),ò0＝0,u0(θ0)＝0. can avoid control signals from exceeding a safety range by introducing soft boundaries and penalty terms, and ensure that the mechanical arm can track a target track quickly and stably.
3. And in the multi-mechanical arm system, the connection relation and the information flow between the mechanical arms are defined by utilizing the concepts of graph theory and network theory, so that the optimization of the cooperative control strategy is facilitated. In the directed communication topology shown in fig. 2, the application example consists of 5 manipulators, indexed 0-4.
Wherein, B= [ 100 1] can be obtained,
4. Experiment verification, namely constructing a simulation model of the multi-mechanical arm system on the MATLAB/Simulink platform, wherein the simulation model comprises tracking precision, response time and calculation load.
Through the improvement, the invention provides a more robust, accurate and efficient multi-mechanical arm tracking control method, which is particularly suitable for the fields of industrial automation, aerospace, medical operation and the like which need high precision and real-time response, and brings remarkable progress to the control technology of a multi-mechanical arm system.
The beneficial effects of the invention are as follows:
1. the tracking precision is remarkably improved, namely the tracking error caused by parameter uncertainty (such as inertia of the mechanical arm and change of friction coefficient) and external interference (such as wind power and vibration) can be compensated in real time through dynamic parameter estimation of the self-adaptive observer, so that the mechanical arm can more accurately follow a preset track, and the operation precision is improved.
2. The introduction of the adaptive observer enhances the robustness of the control system so that it can remain stable in the face of various unknown or varying environmental conditions. This is critical for applications in the fields of industrial automation, aerospace, medical surgery, etc., as these scenarios often contain a large number of unpredictable factors.
3. The cooperative control strategy is designed specifically, so that the multiple mechanical arms can keep coordinated and consistent actions when executing complex tasks, collision risk is reduced, and the overall operation efficiency and safety of the multiple mechanical arm system are improved.
4. Compared with model predictive control, the method reduces the burden of real-time calculation and improves the response speed of the system while ensuring the control effect, which is particularly important for scenes requiring quick response.
5. The implementation of the invention can obviously improve the production efficiency, reduce the rejection rate and reduce the maintenance cost. In the field of medical surgical robots, higher accuracy means less trauma and faster recovery, bringing better treatment experience for the patient, while also reducing medical costs.
The embodiment of the invention provides a control system of a multi-mechanical arm system, which comprises the following components:
The system construction module is used for constructing a nonlinear system which is guided by one leader mechanical arm to move by a plurality of follower mechanical arms in an information interaction mode. The control design module is used for estimating the composite disturbance state of each mechanical arm by using a composite disturbance observer for each mechanical arm in a nonlinear system, respectively using a local state observer and a distributed observer for each mechanical arm to obtain a local state observation error and a distributed observation error of each mechanical arm, and determining the consistent bounded state of each mechanical arm according to the local state observation error and the distributed observation error. The control system construction module is used for enabling the nonlinear system to keep stable running according to the composite disturbance state and the consistent bounded state, and can achieve consistent tracking control of the nonlinear system, wherein the consistent tracking control is used for indicating that all mechanical arms can achieve consistent movement.
The embodiment of the invention provides a computer device which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of a control method of a multi-mechanical arm system when executing the computer program.
The embodiment of the invention provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the steps of a control method of a multi-mechanical arm system when being executed by a processor.
One specific example is as follows:
1. The dynamic parameter self-adaptive mechanism can automatically adapt to the change of the working environment by updating the dynamic parameter estimation of the mechanical arm in real time, thereby reducing the dependence on external calibration and lowering the maintenance cost.
2. The cooperative control optimization algorithm utilizes an advanced optimization algorithm, can intelligently adjust the relative position and motion among the mechanical arms, ensures the high efficiency and safety of the multi-mechanical-arm system when executing tasks, and opens up a new way for the automatic execution of complex tasks.
3. Economic benefit analysis in the field of industrial manufacture, higher precision and efficiency means increased production of the production line and reduced cost. In the medical field, the application of the invention can reduce operation time and complications, improve patient satisfaction, save cost for medical institutions and improve the quality and level of medical service. These factors together promote the dual improvement of economic and social benefits of related industries.
The above examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.

Claims (7)

1.一种多机械臂系统的控制方法，其特征在于，包括以下步骤：1. A control method for a multi-robot system, characterized in that it comprises the following steps: 构建由一个领导者机械臂通过信息交互方式领导多个跟随者机械臂进行运动的非线性系统；Construct a nonlinear system in which a leader robot leads multiple follower robots to move through information interaction; 在非线性系统内，对每个机械臂使用复合扰动观测器估计每个机械臂的复合扰动状态；对每个机械臂分别使用局部状态观测器和分布式观测器，获得每个机械臂的局部状态观测误差和分布式观测误差；根据局部状态观测误差和分布式观测误差确定每个机械臂一致有界状态；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; 根据复合扰动状态和一致有界状态让非线性系统保持稳定运行，能够实现非线性系统的一致性跟踪控制，所述一致性跟踪控制用于表示所有机械臂能够实现一致性运动。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.如权利要求1所述的一种多机械臂系统的控制方法，其特征在于，所述获得每个机械臂的局部状态观测误差和分布式观测误差，具体步骤包括：2. A control method for a multi-manipulator system according to claim 1, characterized in that the step of obtaining the local state observation error and the distributed observation error of each manipulator comprises: 对每个机械臂使用局部状态观测器 Use a local state observer for each robot 定义局部状态观测误差为 The local state observation error is defined as 其中，N表示跟随者机械臂的个数，i表示第i个跟随者机械臂，是状态xi的估计，是状态观测器的输出，ui∈Rp是控制输入，K∈Rn×q是增益矩阵，使得A+KC为赫尔维茨Hurwitz矩阵；θi(t)表示输入时延，并满足θi(t)＝t-τi(t)，τi(t)是非统一时变输入时延；表示不确定外部扰动；和分别是径向基神经网络权值矩阵和激活函数，A∈Rn ×n，B∈Rn×p，C∈Rq×n是常数矩阵；Where N represents the number of follower robotic arms, i represents the i-th follower robotic arm, is the estimate of state xi , is the output of the state observer, ui∈Rp is the control input, K∈Rn ×q is the gain matrix, so that A+KC is the Hurwitz matrix; θi (t) represents the input delay and satisfies θi (t)＝t- τi (t), τi (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 , and C∈R q×n are constant matrices; 对每个机械臂使用分布式观测器 Use a distributed observer for each robot 其中，表示领导者状态的估计，表示分布式观测器的输出，Λ∈Rq×n是增益矩阵，ei是一致性跟踪误差，公式为：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: 定义分布式观测误差为 The distributed observation error is defined as 其中，x0是领导者状态，j表示智能体j索引；Ni表示智能体i的邻域智能体；aij表示智能体i与智能体j之间通信权重；bi表示智能体i与领导者之间通信权重；y0表示领导者输出。Among them, x0 is the leader state, j represents the index of agent j; Ni represents the neighboring agent of agent i; aij represents the communication weight between agent i and agent j; bi represents the communication weight between agent i and the leader; y0 represents the leader output. 3.如权利要求1所述的一种多机械臂系统的控制方法，其特征在于，所述根据局部状态观测误差和分布式观测误差确定每个机械臂一致有界状态，具体步骤包括：3. A control method for a multi-manipulator system according to claim 1, characterized in that the step of determining the consistent bounded state of each manipulator according to the local state observation error and the distributed observation error comprises: 获取局部状态观测误差以及分布式观测误差 Get local state observation error and distributed observation errors 根据两种误差得出 According to the two errors 当时间t趋于无限时，得出且确定每个机械臂一致有界状态。When time t tends to infinity, we get and Determine the consistent and bounded state of each robot. 4.如权利要求1所述的一种多机械臂系统的控制方法，其特征在于，所述实现非线性系统的一致性跟踪控制，具体步骤包括：4. A control method for a multi-manipulator system according to claim 1, characterized in that the steps of realizing the consistency tracking control of the nonlinear system include: 通过复合扰动观测器、局部状态观测器和分布式观测器将非线性系统的一致性跟踪控制问题转换为分析非线性系统的稳定性，获得非线性系统的稳定性模型；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. 所述非线性系统的稳定性模型，公式为：The stability model of the nonlinear system is as follows: 其中，不确定外部扰动；局部状态观测误差；是估计误差，和分别是径向基神经网络权值矩阵和激活函数，表示领导者状态的估计；A∈Rn×n，B∈Rn×p，C∈Rq×n是常数矩阵，K∈Rn×q，Λ∈Rq×n是增益矩阵；ei是一致性跟踪误差；ui∈Rp是控制输入；θi表示输入时延；误差信息，跟随者状态状态估计，表示领导者状态估计；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∈Rn ×n , B∈Rn ×p , C∈Rq ×n are constant matrices, K∈Rn ×q , Λ∈Rq ×n are gain matrices; e i is the consistency tracking error; u i ∈Rp is the control input; θ i represents the input delay; Error information, Follower state state estimation, represents the leader state estimate; 根据非线性系统的稳定性模型构建控制器，所述控制器能够实现非线性系统的一致性跟踪控制；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; 所述控制器，公式为：The controller formula is: 其中，ui∈Rp是控制输入；Γi＝-BTPi，Pi是正定矩阵；ei是一致性跟踪误差；A∈Rn×n是常数矩阵；是输入延时边界为已知常数；误差信息，跟随者状态估计，领导者状态估计。Where, ui∈Rp is the control input; Γi ＝ -BTPi , Pi is a positive definite matrix ; ei is the consistent tracking error; A∈Rn ×n is a constant matrix; The input delay boundary is a known constant; Error information, Follower state estimation, Leader state estimation. 5.一种多机械臂系统的控制系统，其特征在于，包括：5. A control system for a multi-manipulator system, characterized by comprising: 系统构建模块，用于构建由一个领导者机械臂通过信息交互方式领导多个跟随者机械臂进行运动的非线性系统；System building module, used to build a nonlinear system in which a leader robot leads multiple follower robots to move through information interaction; 控制设计模块，用于在非线性系统内，对每个机械臂使用复合扰动观测器估计每个机械臂的复合扰动状态；对每个机械臂分别使用局部状态观测器和分布式观测器，获得每个机械臂的局部状态观测误差和分布式观测误差；根据局部状态观测误差和分布式观测误差确定每个机械臂一致有界状态；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; 控制系统构建模块，用于根据复合扰动状态和一致有界状态让非线性系统保持稳定运行，能够实现非线性系统的一致性跟踪控制，所述一致性跟踪控制用于表示所有机械臂能够实现一致性运动。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. 6.一种计算机设备，包括：存储器和处理器；6. A computer device comprising: a memory and a processor; 所述存储器存储有计算机程序，其特征在于，所述处理器执行所述计算机程序时实现权利要求1～4中任一项所述的一种多机械臂系统的控制方法。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. 7.一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现如权利要求1～4中任一项所述的一种多机械臂系统的控制方法。7. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements a control method for a multi-robot system according to any one of claims 1 to 4.
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