Multi-agent system model prediction consistency control method and device

Abstract

The present invention discloses a multi-agent system model predictive consistency control method and device. The method constructs a multi-agent system model subject to external nonlinear interference during the operation of the multi-agent system; constructs a system state tracking observer scheme based on the multi-agent system model; derives sufficient conditions for the multi-agent system state observer to effectively track the actual state of the multi-agent system under nonlinear interference based on Lyapunov stability theory, and solves the multi-agent system state tracking observer parameters based on these conditions to obtain a multi-agent system state tracking observer model; constructs a multi-agent system consistency control objective function, and completes the design of a multi-agent model predictive consistency controller under nonlinear disturbances based on the multi-agent system state observer model and the model predictive control scheme. While ensuring anti-interference capability, the present invention reduces communication overhead by approximately 20%, making it suitable for distributed scenarios with limited resources.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-agent system control, and in particular discloses a multi-agent system model prediction consistency control method and device. Background Art

[0002] In recent years, with the widespread adoption of intelligent devices with advanced computing, execution, and autonomous perception capabilities, multi-agent collaborative control technology has garnered significant attention from researchers in diverse fields, such as mobile robot control, distributed optimization, and aircraft formation control. Consensus control, a key component of collaborative operational control research, is a crucial topic in the field of multi-agent system control.

[0003] External disturbances in a multi-agent system affect the individual operating states of the system agents and the overall topology of the system, and are an important factor that needs to be considered in achieving consistent control.

[0004] For individual agents in a multi-agent system, unknown external disturbances can lead to inaccurate state models of the system's individual operating states, hindering the achievement of the system's consistent control objectives. To address this issue, researchers typically use Lipschitz conditions to linearize the nonlinear functions (external unknown disturbances) in the model. Zou used Lipschitz conditions to study the leader-follower consistency problem in a second-order nonlinear multi-agent system. Xu combined impulse control, event-triggered control, and sampled data control schemes to study the nonlinear multi-agent consistent control problem using Lipschitz conditions. Zhang used Lipschitz conditions to study the leader-follower distributed control consistency problem for nonlinear multi-agent systems with input delays. The Lipschitz condition introduces a "maximum linearization parameter" to constrain the dynamic range of the nonlinear function to that of the linear function, thereby achieving linearization of the nonlinear function. While this ultimately achieves consistent control for the multi-agent system, the control implementation process is based on the "maximum linearization parameter," increasing control costs. Furthermore, this approach cannot precisely control the system's operating state during operation.

[0005] Regarding the communication topology between agents in a multi-agent system, given the limited communication distance between agents, external disturbances can significantly alter the communication topology, significantly impacting the stability of the consensus control. To address this issue, Cai proposed a novel event-triggered sliding mode control strategy to study the consensus problem of second-order multi-agent systems under switching topologies. Wang employed an extended ring function to solve the problem of fault-tolerant consensus control for non-periodic sampling data in leader-follower nonlinear multi-agent systems with a general uncertain semi-Markov switching topology. Lu proposed a topology switching scheme based on structural balance to achieve finite-time consensus in second-order nonlinear multi-agent systems, considering the transition between communication existence and the cooperative-competitive relationship between followers and leaders. In existing literature, researchers primarily investigate the topology transformation of multi-agent systems through one or more topology switching approaches. However, given the time-varying nature of external disturbances, the topology switching schemes proposed in existing research are not applicable to situations where external disturbances cause the communication topology of the multi-agent system to change moment by moment.

[0006] In order to solve the problem of consistency control caused by changes in the communication topology of multi-agent systems due to external disturbances, a large number of researchers in existing literature have used model predictive control schemes to achieve collaborative or consistent control of multi-agents. Zhang applied the DMPC strategy to the MAS system and solved the consistency problem of first-order and second-order discrete MAS systems. Wang proposed a distributed model predictive control algorithm to solve the optimal consistency problem of asynchronous sampling single integrator and double integrator multi-agent systems. Li proposed a model predictive control synchronous distributed optimization algorithm to study the optimal output consistency problem of high-order multi-agent systems. Wang proposed a distributed model predictive control algorithm for the linear quadratic optimal consistency problem of discrete-time multi-agent systems. Since model predictive control requires the prediction of the future state of the system through the model, it often cannot achieve good results for multi-agent systems under nonlinear disturbances. Summary of the Invention

[0007] The present invention provides a method and device for predictive consistency control of a multi-agent system model, aiming to solve at least one defect existing in the above-mentioned prior art.

[0008] One aspect of the present invention relates to a method for predictive consistency control of a multi-agent system model, comprising the following steps:

[0009] Construct a multi-agent system model subject to external nonlinear interference during the operation of the multi-agent system;

[0010] Based on the multi-agent system model, a system state tracking observer scheme is constructed;

[0011] Based on Lyapunov stability theory, sufficient conditions are derived for the multi-agent system state observer to effectively track the actual state of the multi-agent system under nonlinear disturbances. Based on this, the parameters of the multi-agent system state tracking observer are solved, and the multi-agent system state tracking observer model is derived.

[0012] Construct the multi-agent system consistency control objective function, based on the multi-agent system state observer model and the model predictive control scheme, complete the design of the multi-agent model predictive consistency controller under nonlinear disturbances;

[0013] The effectiveness of the multi-agent system state observer model and the system consistency predictive control scheme is obtained through numerical simulation.

[0014] Furthermore, in the step of constructing a multi-agent system model subjected to external nonlinear interference during the operation of the multi-agent system, the multi-agent system model is:

[0015]

[0016]

[0017]

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024] in, Indicates the operating status of the multi-agent system. express The operating status of the multi-agent system at all times, express The operating status of the multi-agent system at all times, Indicates the sampling period; represents the number of agents in a multi-agent system, R refers to a real number, Represents the individual operating dimension of the intelligent agent, Represents an intelligent individual location, Represents an intelligent individual speed, represents the identity matrix of appropriate dimensions, represents a matrix of all zeros of appropriate dimensions, Indicates the dimension The identity matrix of .

[0025] Furthermore, based on the multi-agent system model, in the step of constructing a system state tracking observer solution, the system state tracking observer is:

[0026]

[0027]

[0028]

[0029]

[0030]

[0031]

[0032] in, express The tracking state of the multi-agent system state tracking observer at each moment, express The tracking state of the multi-agent system state tracking observer at each moment, express The operating status of the multi-agent system at all times, represents the sampling period, is the observation gain to be designed, is the function in the system model The estimation function of .

[0033] Furthermore, based on Lyapunov stability theory, sufficient conditions are obtained for the multi-agent system state observer to effectively track the actual state of the multi-agent system under nonlinear disturbances, and the parameters of the multi-agent system state tracking observer are solved. In the steps of the multi-agent system state tracking observer model, given the relevant parameters of the multi-agent system, if there is a sampling period , positive definite symmetric matrix and feedback observation gain , so that the following inequality holds, the error system is asymptotically stable, that is, the observer model performs effective tracking:

[0034]

[0035]

[0036]

[0037]

[0038]

[0039]

[0040] in, represents the positive constant to be solved, represents the parameters of the Lyapunov functional, is the positive definite symmetric matrix to be solved, R refers to a real number, Represents the individual operating dimension of the intelligent agent, represents the identity matrix of appropriate dimensions, represents a matrix of all zeros of appropriate dimensions, Indicates the dimension The identity matrix, represents the sampling period, is the observation gain to be designed, Lg1 and Lg2 represent the estimation function With nonlinear functions The linearization parameter in the linearization process of the error function between .

[0041] Furthermore, the consensus control objective function of the multi-agent system is constructed. Based on the multi-agent system state observer model and the model predictive control scheme, the design of the multi-agent model predictive consistency controller under nonlinear disturbance is completed. The control input component corresponding to the system at the current moment is obtained. The control input component is:

[0042]

[0043] in, represents the control input of the system at the current moment, Indicates the dimension The identity matrix, express An all-zero matrix of dimension , Represents the input quantity within the predictive control step time series of the multi-agent system model.

[0044] Another aspect of the present invention relates to a multi-agent system model prediction consistency control device based on a state tracking observer, comprising:

[0045] The first building module is used to build a multi-agent system model that is subject to external nonlinear interference during the operation of the multi-agent system;

[0046] The second building module is used to build a system state tracking observer solution based on the multi-agent system model;

[0047] The acquisition module is used to derive sufficient conditions for the multi-agent system state observer to effectively track the actual state of the multi-agent system under nonlinear disturbances based on Lyapunov stability theory, and to solve the multi-agent system state tracking observer parameters and derive the multi-agent system state tracking observer model.

[0048] The third building block is used to construct the multi-agent system consistency control objective function. Based on the multi-agent system state observer model and the model predictive control scheme, it completes the design of the multi-agent model predictive consistency controller under nonlinear disturbances.

[0049] The simulation module is used to obtain the effectiveness of the multi-agent system state observer model and the system consistency predictive control scheme through numerical simulation.

[0050] Furthermore, in the first building block, the multi-agent system model is:

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060] in, Indicates the operating status of the multi-agent system. express The operating status of the multi-agent system at all times, express The operating status of the multi-agent system at all times, Indicates the sampling period; represents the number of agents in a multi-agent system, R refers to a real number, Represents the individual operating dimension of the intelligent agent, Represents an intelligent individual location, Represents an intelligent individual speed, represents the identity matrix of appropriate dimensions, represents a matrix of all zeros of appropriate dimensions, Indicates the dimension The identity matrix of .

[0061] Furthermore, in the second building block, the system state tracking observer is:

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068] in, express The tracking state of the multi-agent system state tracking observer at each moment, express The tracking state of the multi-agent system state tracking observer at each moment, express The operating status of the multi-agent system at all times, represents the sampling period, is the observation gain to be designed, is the function in the system model The estimation function of .

[0069] Furthermore, in the acquisition module, given the relevant parameters of the multi-agent system, if there is a sampling period , positive definite symmetric matrix and feedback observation gain , so that the following inequality holds, the error system is asymptotically stable, that is, the observer model performs effective tracking:

[0070]

[0071]

[0072]

[0073]

[0074]

[0075]

[0076] in, represents the positive constant to be solved, represents the parameters of the Lyapunov functional, is the positive definite symmetric matrix to be solved, R refers to a real number, Represents the individual operating dimension of the intelligent agent, represents the identity matrix of appropriate dimensions, represents a matrix of all zeros of appropriate dimensions, Indicates the dimension The identity matrix, represents the sampling period, is the observation gain to be designed, Lg1 and Lg2 represent the estimation function With nonlinear functions The linearization parameter in the linearization process of the error function between .

[0077] Furthermore, in the third building block, the control input component corresponding to the system at the current moment is obtained, and the control input component is:

[0078]

[0079] in, represents the control input of the system at the current moment, Indicates the dimension The identity matrix, express An all-zero matrix of dimension , Represents the input quantity within the predictive control step time series of the multi-agent system model.

[0080] The beneficial effects achieved by the present invention are:

[0081] The present invention provides a multi-agent system model predictive consistency control method and device, which adopts the method of constructing a multi-agent system model that is subject to external nonlinear interference during the operation of the multi-agent system; constructing a system state tracking observer scheme based on the multi-agent system model; based on the Lyapunov stability theory, obtaining sufficient conditions for the multi-agent system state observer to effectively track the actual state of the multi-agent system under nonlinear interference, and solving the multi-agent system state tracking observer parameters based on this to obtain the multi-agent system state tracking observer model; constructing the multi-agent system consistency control objective function, based on the multi-agent system state observer model, and completing the design of the multi-agent model predictive consistency controller under nonlinear disturbance based on the model predictive control scheme; and obtaining the effectiveness of the multi-agent system state observer model and the system consistency predictive control scheme through numerical simulation. The model predictive consistency control method and device for a multi-agent system provided by the present invention construct a model predictive control scheme for a multi-agent system under nonlinear disturbances, solve the problem of multi-agent consistency control when the communication topology of the multi-agent system changes at all times due to external disturbances, and provide a new idea for the application of model predictive control in the field of nonlinear system control. Through the collaborative design of interference observation and predictive control, the system reduces communication overhead by about 20% while ensuring anti-interference capability, and is suitable for distributed scenarios with limited resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 2. It is a flow chart of an embodiment of a method for predictive consistency control of a multi-agent system model according to the present invention. DETAILED DESCRIPTION

[0083] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0084] like Figure 1 As shown, the first embodiment of the present invention proposes a multi-agent system model prediction consistency control method, comprising the following steps:

[0085] Step S100: construct a multi-agent system model that is subject to external nonlinear interference during the operation of the multi-agent system.

[0086] According to the characteristics of external nonlinear interference, a multi-agent system model based on empirical function approximation is constructed.

[0087] A multi-agent system (MAS) is a distributed computing system composed of multiple autonomous agents (agents). Its core characteristics lie in individual autonomy and group collaboration. Each agent has the ability to independently perceive the environment, learn and evolve, and make decisions. It also dynamically collaborates with other agents or the environment through interaction rules (such as communication protocols and coordination mechanisms) to ultimately achieve local or global goals.

[0088] Step S200: Based on the multi-agent system model, a system state tracking observer solution is constructed.

[0089] Based on the multi-agent system model, a system state tracking observer solution is constructed. The system state tracking observer is a dynamic system whose core function is to estimate or reconstruct the system's internal state variables, which cannot be directly measured, in real time through external measurable variables (such as input and output signals). On this basis, it can also achieve the ability to track external reference trajectories or target states.

[0090] Step S300: Based on Lyapunov stability theory, sufficient conditions are obtained for the multi-agent system state observer to effectively track the actual state of the multi-agent system under nonlinear interference, and the parameters of the multi-agent system state tracking observer are solved to obtain the multi-agent system state tracking observer model.

[0091] Based on Lyapunov stability theory, sufficient conditions are derived for the state tracking observer to be able to track and observe the state of a multi-agent system.

[0092] Lyapunov stability theory is a mathematical framework for analyzing the stability of dynamic systems. Proposed by Russian mathematician A.M. Lyapunov in 1892, it is used to characterize the dynamic behavior of a system around its equilibrium point. Its core concept is to construct a scalar function (Lyapunov function) to analyze the energy trends of a system, thereby indirectly deriving stability and avoiding the complexity of directly solving differential equations.

[0093] Step S400: Construct a multi-agent system consistency control objective function, based on the multi-agent system state observer model and the model predictive control scheme, complete the design of the multi-agent model predictive consistency controller under nonlinear disturbances.

[0094] The objective function for consensus control in a multi-agent system (MAS) is a mathematical expression used to quantify the convergence of system states to consistency. Its core goal is to drive the states (such as position, velocity, and direction) of all agents toward the same or coordinated desired values ​​through optimization. The specific definition requires a comprehensive design based on the type of consensus, the system dynamics model, and the control requirements.

[0095] Model Predictive Control (MPC) is an advanced control strategy based on dynamic models, rolling optimization, and feedback correction. Its core idea is to achieve high-performance control of complex systems by solving open-loop optimal control problems within a finite time domain online, generating and executing control instructions in real time.

[0096] Step S500: Determine the effectiveness of the multi-agent system state observer model and the system consistency prediction control scheme through numerical simulation.

[0097] The "Consistency Predictive Control" approach is a novel control strategy that integrates a predictive control framework with the goal of multi-agent collaboration. Through distributed model prediction and rolling optimization, it aims to drive the states (such as position, velocity, and direction) of multiple agents to converge to consistent or coordinated dynamic trajectories within a finite time domain. Its core approach is to embed the consistency control requirement into the predictive model and optimization objective function, enabling collaborative and adaptive control of multi-agent systems.

[0098] Furthermore, in the multi-agent system model prediction consistency control method proposed in this embodiment, in step S100, the multi-agent system model is:

[0099] (1)

[0100] (2)

[0101] (3)

[0102] (4)

[0103] (5)

[0104] (6)

[0105] (7)

[0106] (8)

[0107] (9)

[0108] In formulas (1)~(9), Indicates the operating status of the multi-agent system. express The operating status of the multi-agent system at all times, express The operating status of the multi-agent system at all times, Indicates the sampling period; represents the number of agents in a multi-agent system, R refers to a real number, Represents the individual operating dimension of the intelligent agent, Represents an intelligent individual location, Represents an intelligent individual speed, represents the identity matrix of appropriate dimensions, represents a matrix of all zeros of appropriate dimensions, Indicates the dimension The multi-agent system model is based on Discretize the multi-agent system for the sampling period. in, is a discrete sampling moment, satisfying .

[0109] Preferably, in the multi-agent system model prediction consistency control method proposed in this embodiment, in step S200, the system state tracking observer is:

[0110] (10)

[0111] (11)

[0112] (12)

[0113] (13)

[0114] (14)

[0115] (15)

[0116] In formulas (10)~(15), express The tracking state of the multi-agent system state tracking observer at each moment, express The tracking state of the multi-agent system state tracking observer at each moment, express The operating state of the multi-agent system at a given moment, R refers to a real number, represents the number of agents in a multi-agent system, Represents the individual operating dimension of the intelligent agent, represents the sampling period, is the observation gain to be designed, is the function in the system model The estimation function of .

[0117] Furthermore, in the multi-agent system model prediction consistency control method proposed in this embodiment, in step S300, given the relevant parameters of the multi-agent system, if there is a sampling period , positive definite symmetric matrix and feedback observation gain , so that the following inequality holds, the error system is asymptotically stable, that is, the observer model performs effective tracking:

[0118] (16)

[0119] (17)

[0120] (18)

[0121] (19)

[0122] (20)

[0123] (twenty one)

[0124] In formulas (16)~(21), represents the positive constant to be solved, represents the Lyapunov functional parameter, is the positive definite symmetric matrix to be solved, represents the number of agents in a multi-agent system, R refers to a real number, Represents the individual operating dimension of the intelligent agent, represents the identity matrix of appropriate dimensions, represents a matrix of all zeros of appropriate dimensions, Indicates the dimension The identity matrix, represents the sampling period, is the observation gain to be designed, Lg1 and Lg2 represent the estimation function With nonlinear functions The linearization parameter in the linearization process of the error function between .

[0125] Assumption 1 is met:

[0126] Assumption 1: is a nonlinear function, for Estimation function, and satisfy:

[0127] (twenty two)

[0128] In formula (22), Indicates the current position state of the multi-agent system, Indicates the speed state of the multi-agent system at the current moment, Represents the observation model's response to the system state The observation state, Represents the observation model's response to the system state Observation status.

[0129] Furthermore, in the multi-agent system model prediction consistency control method proposed in this embodiment, in step S400, the multi-agent system consistency control objective function constructed is:

[0130] (twenty three)

[0131] In formula (23), represents the prediction cost function of the multi-agent system consistency control model, Represents the individual agent Pointing to the intelligent individual vector, Represents the individual agent Pointing to the intelligent individual The unit vector of represents the expected edge length between the consensus control agents in the multi-agent system, 、 are the prediction step size and the control step size, respectively. represents the average speed of all individuals, is the weighting coefficient of velocity error, is the control weighting coefficient.

[0132] In step S400, the control input component corresponding to the current system is obtained, and the control input component is:

[0133] (twenty four)

[0134] In formula (24), represents the control input of the system at the current moment, Indicates the dimension The identity matrix, express An all-zero matrix of dimension , Represents the input quantity within the predictive control step time series of the multi-agent system model.

[0135] The present invention relates to a multi-agent system model prediction consistency control device based on a state tracking observer, comprising a first building module, a second building module, an acquisition module, a third building module and a simulation module, wherein the first building module is used to construct a multi-agent system model subject to external nonlinear interference during the operation of the multi-agent system; the second building module is used to construct a system state tracking observer scheme based on the multi-agent system model; the acquisition module is used to derive sufficient conditions for the multi-agent system state observer to effectively track the actual state of the multi-agent system under nonlinear interference based on Lyapunov stability theory, and thereby solve the multi-agent system state tracking observer parameters to obtain the multi-agent system state tracking observer model; the third building module is used to construct a multi-agent system consistency control objective function, and based on the multi-agent system state observer model, completes the design of the multi-agent model prediction consistency controller under nonlinear disturbance based on the model predictive control scheme; the simulation module is used to derive the effectiveness of the multi-agent system state observer model and the system consistency predictive control scheme through numerical simulation.

[0136] Furthermore, in the multi-agent system model prediction consistency control device based on the state tracking observer provided in this embodiment, in the first building block, the multi-agent system model is:

[0137] (25)

[0138] (26)

[0139] (27)

[0140] (28)

[0141] (29)

[0142] (30)

[0143] (31)

[0144] (32)

[0145] (33)

[0146] In formulas (25)~(33), Indicates the operating status of the multi-agent system. express The operating status of the multi-agent system at all times, express The operating status of the multi-agent system at all times, Indicates the sampling period; represents the number of agents in a multi-agent system, R refers to a real number, Represents the individual operating dimension of the intelligent agent, Represents an intelligent individual location, Represents an intelligent individual speed, represents the identity matrix of appropriate dimensions, represents a matrix of all zeros of appropriate dimensions, Indicates the dimension The multi-agent system model is based on Discretize the multi-agent system for the sampling period. in, is a discrete sampling moment, satisfying .

[0147] Furthermore, in the multi-agent system model prediction consistency control device based on the state tracking observer provided by this embodiment, in the second building block, the system state tracking observer is:

[0148] (34)

[0149] (35)

[0150] (36)

[0151] (37)

[0152] (38)

[0153] (39)

[0154] In formulas (34)~(39), express The tracking state of the multi-agent system state tracking observer at each moment, express The tracking state of the multi-agent system state tracking observer at each moment, express The operating state of the multi-agent system at a given moment, R refers to a real number, represents the number of agents in a multi-agent system, Represents the individual operating dimension of the intelligent agent, represents the sampling period, is the observation gain to be designed, is the function in the system model The estimation function of .

[0155] Preferably, the multi-agent system model prediction consistency control device based on state tracking observer provided in this embodiment, in the acquisition module, given the multi-agent system related parameters, if there is a sampling period , positive definite symmetric matrix and feedback observation gain , so that the following inequality holds, the error system is asymptotically stable, that is, the observer model performs effective tracking:

[0156] (40)

[0157] (41)

[0158] (42)

[0159] (43)

[0160] (44)

[0161] (45)

[0162] In formulas (40)~(45), represents the positive constant to be solved, represents the Lyapunov functional parameter, is the positive definite symmetric matrix to be solved, represents the number of agents in a multi-agent system, R refers to a real number, Represents the individual operating dimension of the intelligent agent, represents the identity matrix of appropriate dimensions, represents a matrix of all zeros of appropriate dimensions, Indicates the dimension The identity matrix, represents the sampling period, is the observation gain to be designed, Lg1 and Lg2 represent the estimation function With nonlinear functions The linearization parameter in the linearization process of the error function between .

[0163] Assumption 1 is met:

[0164] Assumption 1: is a nonlinear function, for Estimation function, and satisfy:

[0165] (46)

[0166] In formula (46), Indicates the current position state of the multi-agent system, Indicates the speed state of the multi-agent system at the current moment, Represents the observation model's response to the system state The observation state, Represents the observation model's response to the system state Observation status.

[0167] Furthermore, in the multi-agent system model prediction consistency control device based on the state tracking observer provided in this embodiment, in the third building block, the multi-agent system consistency control objective function constructed is:

[0168] (47)

[0169] In formula (47), represents the prediction cost function of the multi-agent system consistency control model, Represents the individual agent Pointing to the intelligent individual vector, Represents the individual agent Pointing to the intelligent individual The unit vector of represents the expected edge length between the consensus control agents in the multi-agent system, 、 are the prediction step size and the control step size, respectively. represents the average speed of all individuals, is the weighting coefficient of velocity error, is the control weighting coefficient.

[0170] Obtain the control input component corresponding to the system at the current moment. The control input component is:

[0171] (48)

[0172] In formula (48), represents the control input of the system at the current moment, Indicates the dimension The identity matrix, express An all-zero matrix of dimension , Represents the input quantity within the predictive control step time series of the multi-agent system model.

[0173] Compared with the existing technology, the multi-agent system model predictive consistency control method and device provided in this embodiment adopts the method of constructing a multi-agent system model that is subject to external nonlinear interference during the operation of the multi-agent system; constructing a system state tracking observer scheme based on the multi-agent system model; based on the Lyapunov stability theory, deriving sufficient conditions for the multi-agent system state observer to effectively track the actual state of the multi-agent system under nonlinear interference, and solving the multi-agent system state tracking observer parameters based on this to obtain the multi-agent system state tracking observer model; constructing the multi-agent system consistency control objective function, based on the multi-agent system state observer model, and completing the design of the multi-agent model predictive consistency controller under nonlinear disturbance based on the model predictive control scheme; and obtaining the effectiveness of the multi-agent system state observer model and the system consistency predictive control scheme through numerical simulation. The multi-agent system model predictive consistency control method and device provided in this embodiment construct a model predictive control scheme for the multi-agent system under nonlinear disturbances, solves the multi-agent consistency control problem when the communication topology of the multi-agent system changes at all times due to external disturbances, and provides a new idea for the application of model predictive control in the field of nonlinear system control. Through the collaborative design of interference observation and predictive control, the system reduces the communication overhead by about 20% while ensuring the anti-interference capability, which is suitable for distributed scenarios with limited resources.

[0174] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they are aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the invention. Thus, the present invention is intended to include such changes and modifications as fall within the scope of the claims and their equivalents.

Claims

1. A multi-agent system model prediction consistency control method, characterized in that: The following steps are involved: Construct a multi-agent system model subject to external nonlinear interference during the operation of the multi-agent system; Based on the multi-agent system model, a system state tracking observer scheme is constructed; Based on Lyapunov stability theory, sufficient conditions are derived for the multi-agent system state observer to effectively track the actual state of the multi-agent system under nonlinear disturbances. Based on this, the parameters of the multi-agent system state tracking observer are solved, and the multi-agent system state tracking observer model is derived. Construct the multi-agent system consistency control objective function, based on the multi-agent system state observer model and the model predictive control scheme, complete the design of the multi-agent model predictive consistency controller under nonlinear disturbances; The effectiveness of the multi-agent system state observer model and the system consistency predictive control scheme is obtained through numerical simulation; Among them, based on the Lyapunov stability theory, the sufficient conditions for the multi-agent system state observer to effectively track the actual state of the multi-agent system under nonlinear interference are obtained, and the multi-agent system state tracking observer parameters are solved based on this, and the multi-agent system state tracking observer model is obtained, which includes: Given the relevant parameters of the multi-agent system, if there is a sampling period , positive definite symmetric matrix and feedback observation gain , so that the following inequality holds, the error system is asymptotically stable, that is, the observer model performs effective tracking: in, represents the positive constant to be solved, represents the Lyapunov functional parameter, is the positive definite symmetric matrix to be solved, represents the number of agents in a multi-agent system, R refers to a real number, Represents the individual operating dimension of the intelligent agent, represents the identity matrix of appropriate dimensions, represents a matrix of all zeros of appropriate dimensions, Indicates the dimension The identity matrix, represents the sampling period, is the observation gain to be designed, Lg1 and Lg2 represent the estimation function With nonlinear functions The linearization parameter in the linearization process of the error function between .

2. The multi-agent system model prediction consistency control method according to claim 1, characterized in that: In the step of constructing a multi-agent system model subject to external nonlinear interference during the operation of the multi-agent system, the multi-agent system model is: in, Indicates the operating status of the multi-agent system. express The operating status of the multi-agent system at all times, express The operating status of the multi-agent system at all times, Indicates the sampling period; represents the number of agents in a multi-agent system, R refers to a real number, Represents the individual operating dimension of the intelligent agent, Represents an intelligent individual location, Represents an intelligent individual speed, represents the identity matrix of appropriate dimensions, represents a matrix of all zeros of appropriate dimensions, Indicates the dimension The identity matrix of .

3. The multi-agent system model prediction consistency control method according to claim 1, characterized in that: In the step of constructing a system state tracking observer based on the multi-agent system model, the system state tracking observer is: in, express The tracking state of the multi-agent system state tracking observer at each moment, express The tracking state of the multi-agent system state tracking observer at each moment, express The operating state of the multi-agent system at a given moment, R refers to a real number, represents the number of agents in a multi-agent system, Represents the individual operating dimension of the intelligent agent, represents the sampling period, is the observation gain to be designed, is the function in the system model The estimation function of .

4. The multi-agent system model prediction consistency control method according to claim 1, characterized in that: In the step of constructing the multi-agent system consistency control objective function, based on the multi-agent system state observer model and the model predictive control scheme, completing the design of the multi-agent model predictive consistency controller under nonlinear disturbance, the control input component corresponding to the system at the current moment is obtained. The control input component is: in, represents the control input of the system at the current moment, Indicates the dimension The identity matrix, express An all-zero matrix of dimension , Represents the input quantity within the predictive control step time series of the multi-agent system model.

5. A multi-agent system model prediction consistency control device based on state tracking observer, characterized in that: include: The first building module is used to build a multi-agent system model that is subject to external nonlinear interference during the operation of the multi-agent system; A second building module is used to build a system state tracking observer solution based on the multi-agent system model; The acquisition module is used to derive sufficient conditions for the multi-agent system state observer to effectively track the actual state of the multi-agent system under nonlinear disturbances based on Lyapunov stability theory, and to solve the multi-agent system state tracking observer parameters and derive the multi-agent system state tracking observer model. The third building block is used to construct the multi-agent system consistency control objective function, based on the multi-agent system state observer model and the model predictive control scheme, to complete the design of the multi-agent model predictive consistency controller under nonlinear disturbances; A simulation module, configured to determine the effectiveness of the multi-agent system state observer model and the system consistency predictive control scheme through numerical simulation; Among them, given the relevant parameters of the multi-agent system, if there is a sampling period , positive definite symmetric matrix and feedback observation gain , so that the following inequality holds, the error system is asymptotically stable, that is, the observer model performs effective tracking: in, represents the positive constant to be solved, represents the Lyapunov functional parameter, is the positive definite symmetric matrix to be solved, represents the number of agents in a multi-agent system, R refers to a real number, Represents the individual operating dimension of the intelligent agent, represents the identity matrix of appropriate dimensions, represents a matrix of all zeros of appropriate dimensions, Indicates the dimension The identity matrix, represents the sampling period, is the observation gain to be designed, Lg1 and Lg2 represent the estimation function With nonlinear functions The linearization parameter in the linearization process of the error function between .

6. The multi-agent system model prediction consistency control device based on state tracking observer according to claim 5, characterized in that: In the first building block, the multi-agent system model is: in, Indicates the operating status of the multi-agent system. express The operating status of the multi-agent system at all times, express The operating status of the multi-agent system at all times, Indicates the sampling period; represents the number of agents in a multi-agent system, R refers to a real number, Represents the individual operating dimension of the intelligent agent, Represents an intelligent individual location, Represents an intelligent individual speed, represents the identity matrix of appropriate dimensions, represents a matrix of all zeros of appropriate dimensions, Indicates the dimension The identity matrix of .

7. The multi-agent system model prediction consistency control device based on state tracking observer according to claim 5, characterized in that: In the second building block, the system state tracking observer is: in, express The tracking state of the multi-agent system state tracking observer at each moment, express The tracking state of the multi-agent system state tracking observer at each moment, express The operating state of the multi-agent system at a given moment, R refers to a real number, represents the number of agents in a multi-agent system, Represents the individual operating dimension of the intelligent agent, represents the sampling period, is the observation gain to be designed, is the function in the system model The estimation function of .

8. The multi-agent system model prediction consistency control device based on state tracking observer according to claim 5, characterized in that: In the third building block, the control input component corresponding to the system at the current moment is obtained, and the control input component is: in, represents the control input of the system at the current moment, Indicates the dimension The identity matrix, express An all-zero matrix of dimension , Represents the input quantity within the predictive control step time series of the multi-agent system model.

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