Intelligent agent cluster time-varying formation control method and system considering anti-interference and anti-delay

Abstract

The invention belongs to the technical field of multi-agent cooperative control, and particularly relates to an anti-interference and anti-delay agent cluster time-varying formation control method and system, and the method comprises the steps: obtaining a dynamic model of a single agent, and constructing an agent cluster queue; calculating a composite control protocol of the agent cluster queue based on the constructed agent cluster queue; according to the obtained composite control protocol, constructing a tracking error of the intelligent agent cluster time-varying formation; analyzing the constructed tracking error to obtain a feedback gain matrix of time-varying formation control; optimizing feedback gain matrix parameters obtained by iteration to obtain an optimal feedback gain matrix; and performing cluster time-varying formation control of the intelligent agents according to the obtained optimal feedback gain matrix, and completing the intelligent agent cluster time-varying formation control considering anti-interference and anti-delay.

Description

Technical Field

[0001] This invention belongs to the field of multi-agent cooperative control technology, specifically relating to a time-varying formation control method and system for agent clusters that considers disturbance rejection and time delay resistance. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With the development of unmanned systems, automation technology, and artificial intelligence, multi-agent systems have become a research hotspot in academia and industry due to their enormous application potential in military and civilian fields such as satellite formation, drone swarm collaboration, autonomous vehicle fleets, and robot clusters. In complex mission scenarios, the capabilities of a single agent are limited, requiring multiple agents to cooperate and form specific formations to achieve functions such as target acquisition, collaborative search, area coverage, and collective obstacle avoidance. Therefore, how to enable a group of agents to achieve a preset, and even time-varying, formation configuration in a distributed and robust manner is a key scientific problem in the control of multi-agent systems.

[0004] In practical applications, information exchange between agents is usually conducted through networks, inevitably introducing communication delays. Simultaneously, external environmental disturbances and the complex nonlinear dynamics of the agents themselves are prevalent. Communication delays and external disturbances can significantly degrade the performance of closed-loop systems, even leading to system instability and formation disintegration. Many existing time-varying formation control methods often assume perfect communication or very small delays, which are highly limiting in practical engineering. Even in the few studies that consider delays and disturbances, the maximum allowable delay boundary is usually small, or it is necessary to meet pre-set, large disturbance suppression targets (such as...). (Performance parameters), sacrificing the actual performance of the formation controller, resulting in insufficient robustness.

[0005] Intelligent agents (such as drones and robots) often possess inherent nonlinear dynamics. Traditional control design often assumes that the nonlinearity satisfies the Lipschitz condition, but this condition is conservative for many practical nonlinear systems, potentially masking more exploitable nonlinear characteristics and thus limiting controller performance. In recent years, a condition called the one-sided Lipschitz condition has been proposed. Compared to the classic Lipschitz condition, it can describe a wider range of nonlinear classes and introduces less conservatism in controller design, especially suitable for systems with large Lipschitz constants.

[0006] Meanwhile, in controller design, the selection of gain parameters directly affects system performance. Traditional methods either fail to consider parameter optimization or rely on experience and random parameter selection, making it difficult to obtain a formation controller with optimal robust performance in dealing with large delays and strong interference.

[0007] Therefore, there is an urgent need for a method that can design and optimize a highly robust time-varying formation controller for agent swarms with large time-varying communication delays, external interference, and more general nonlinear dynamics, so as to improve the collaborative task execution capability of agent swarms in complex and constrained environments. Summary of the Invention

[0008] To address the aforementioned issues, this invention proposes a time-varying formation control method and system for intelligent agent clusters that considers disturbance rejection and time delay resistance. The method employs a particle swarm optimization algorithm to optimize control parameters, thereby achieving robust time-varying formation control of intelligent agent clusters and improving overall robustness and convergence speed.

[0009] According to some embodiments, the first aspect of the present invention provides a time-varying formation control method for intelligent agent clusters that considers disturbance rejection and time delay resistance, employing the following technical solution: A time-varying formation control method for intelligent agent swarms considering disturbance rejection and latency resistance includes: Obtain the dynamic model of a single agent and construct an agent cluster queue; Based on the constructed agent cluster queue, compute the composite control protocol of the agent cluster queue; Based on the obtained composite control protocol, the tracking error of the time-varying formation of the intelligent agent cluster is constructed; By analyzing the constructed tracking error, the feedback gain matrix of time-varying formation control is obtained; The optimal feedback gain matrix is ​​obtained by optimizing the parameters of the feedback gain matrix obtained through iteration. Based on the obtained optimal feedback gain matrix, the agent cluster time-varying formation control is performed to complete the agent cluster time-varying formation control considering disturbance rejection and time delay.

[0010] As a further technical limitation, the obtained dynamic model of a single agent is as follows: ;in, For the first The state vector of each agent. For the first The control input vector of an agent. To act on the first External interference to an intelligent agent; These are constant matrices with appropriate dimensions; It is a nonlinear function that satisfies the one-sided Lipschitz condition and the quadratic inner bound constraint.

[0011] Furthermore, the resulting composite control protocol for the agent cluster queue is as follows: ;in, For the first The offset vector of each agent in the desired formation For formation reference trajectory, For time-varying communication delay and satisfying , The feedback gain matrix to be designed, For coupling strength parameters, For formation tracking compensation function, These are elements of the communication topology adjacency matrix.

[0012] Furthermore, the first i The formation tracking error of each agent is ;make , , Substituting the composite control protocol into the dynamic model, and combining it with the properties of the Laplace matrix... This yields a compact form of the closed-loop error system. .

[0013] As a further technical limitation, robustness analysis of the tracking error is conducted based on Lyapunov-Krasovskii functionals and linear matrix inequalities. Specifically, an augmented Lyapunov-Krasovskii functional is constructed, and combined with a novel generalized free matrix integral inequality for handling integral terms and a relaxed negative definite lemma for quadratic functions, a derive is made that allows the formation tracking error system to satisfy predetermined conditions under the presence of external disturbances and time-varying delays. Sufficient conditions for interference suppression performance; the sufficient conditions are expressed as a set of nonlinear matrix inequalities with respect to the controller gain matrix and the functional correlation matrix.

[0014] As a further technical limitation, the obtained feedback gain matrix parameters are optimized iteratively using the particle swarm optimization algorithm. The feedback gain matrix parameters are used as optimization variables, and the integral performance index of the formation tracking error is used as the fitness function. The parameter combination is iteratively searched through the particle swarm optimization algorithm to find the parameter combination that makes the tracking performance optimal, and thus the optimal feedback gain matrix is ​​obtained.

[0015] According to some embodiments, a second aspect of the present invention provides a time-varying formation control system for intelligent agent clusters that considers disturbance rejection and time delay resistance, employing the following technical solution: A time-varying formation control system for intelligent agent swarms, considering disturbance rejection and latency resistance, includes: The acquisition module is configured to acquire the dynamic model of a single agent and build an agent cluster queue. The computing module is configured to compute the composite control protocol of the constructed agent cluster queue based on the agent cluster queue. The building module is configured to construct the tracking error of the time-varying formation of the agent cluster based on the obtained composite control protocol; The analysis module is configured to analyze the constructed tracking error to obtain the feedback gain matrix for time-varying formation control; The optimization module is configured with the parameters of the feedback gain matrix obtained from the optimization iteration to obtain the optimal feedback gain matrix. The control module is configured to perform time-varying formation control of the agent cluster based on the obtained optimal feedback gain matrix, thus completing time-varying formation control of the agent cluster considering disturbance rejection and time delay resistance.

[0016] According to some embodiments, a third aspect of the present invention provides a computer-readable storage medium, employing the following technical solution: A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the time-varying formation control method for intelligent agent clusters considering disturbance rejection and latency as described in the first aspect of the present invention.

[0017] According to some embodiments, the fourth aspect of the present invention provides an electronic device, which adopts the following technical solution: An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, it implements the steps in the time-varying formation control method for intelligent agent clusters that considers anti-disturbance and anti-delay as described in the first aspect of the present invention.

[0018] According to some embodiments, the fifth aspect of the present invention provides a computer program product, which adopts the following technical solution: A computer program product includes software code, wherein the program in the software code performs the steps of the time-varying formation control method for intelligent agent clusters considering disturbance rejection and time delay as described in the first aspect of the present invention.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention effectively solves the formation control problem of multi-agent systems with large-scale time-varying communication delays, external interference, and complex nonlinearities by employing a one-sided Lipschitz nonlinear model, designing a composite anti-delay control protocol, constructing an advanced Lyapunov functional for analysis, and integrating a particle swarm optimization algorithm for parameter optimization. It significantly improves the robustness, anti-interference ability, and engineering practicality of the system. Attached Figure Description

[0020] The accompanying drawings, which form part of this embodiment, are used to provide a further understanding of this embodiment. The illustrative embodiments and their descriptions are used to explain this embodiment and do not constitute an improper limitation of this embodiment.

[0021] Figure 1 This is a flowchart of the intelligent agent cluster time-varying formation control method considering anti-disturbance and anti-delay in Embodiment 1 of the present invention; Figure 2 This is a detailed schematic diagram illustrating the steps of the time-varying formation control method for intelligent agent clusters that considers anti-disturbance and anti-delay in Embodiment 1 of the present invention. Figure 3 This is a schematic diagram of the desired hexagonal time-varying formation configuration in Embodiment 1 of the present invention; Figure 4 This is a snapshot of the formation position trajectory of the intelligent agent cluster in Embodiment 1 of the present invention at h=0.6788 seconds; Figure 5 As in Embodiment 1 of the present invention Figure 4 An enlarged view of the formation configuration at a specific moment, showing a schematic diagram of the formation successfully forming a hexagonal configuration; Figure 6 This is a schematic diagram of the eastern position trajectory of the UAV under the control of the controller in Embodiment 1 of the present invention; Figure 7 This is a schematic diagram of the eastern velocity trajectory of the UAV under the control of the controller in Embodiment 1 of the present invention; Figure 8 This is a schematic diagram of the northern position trajectory of the UAV under the control of the controller in Embodiment 1 of the present invention; Figure 9 This is a schematic diagram of the northern speed trajectory of the UAV under the control of the controller in Embodiment 1 of the present invention; Figure 10 This is a schematic diagram of the fitness evolution curve during the iterative process of the particle swarm optimization algorithm in Embodiment 1 of the present invention; Figure 11 This is a schematic diagram comparing the formation geometry center tracking error curves of the controller before and after optimization in Embodiment 1 of the present invention; Figure 12 This is a structural block diagram of the intelligent agent cluster time-varying formation control system considering anti-disturbance and anti-delay in Embodiment 2 of the present invention. Detailed Implementation

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0024] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0025] In this invention, terms such as "upper," "lower," "left," "right," "front," "back," "vertical," "horizontal," "side," and "bottom" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used only to facilitate the description of the structural relationships of the various components or elements of this invention and do not specifically refer to any component or element in this invention. They should not be construed as limiting the invention.

[0026] In this invention, terms such as "fixed connection," "connected," and "linked" should be interpreted broadly, indicating a fixed connection, an integral connection, or a detachable connection; a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can determine the specific meaning of these terms in this invention based on the specific circumstances, and they should not be construed as limitations on the invention.

[0027] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0028] Example 1 Embodiment 1 of this invention introduces a time-varying formation control method for intelligent agent clusters that considers disturbance rejection and time delay resistance.

[0029] like Figure 1 The method for time-varying formation control of intelligent agent swarms considering disturbance rejection and delay resistance, as shown, includes: Obtain the dynamic model of a single agent and construct an agent cluster queue; Based on the constructed agent cluster queue, compute the composite control protocol of the agent cluster queue; Based on the obtained composite control protocol, the tracking error of the time-varying formation of the intelligent agent cluster is constructed; By analyzing the constructed tracking error, the feedback gain matrix of time-varying formation control is obtained; The optimal feedback gain matrix is ​​obtained by optimizing the parameters of the feedback gain matrix obtained through iteration. Based on the obtained optimal feedback gain matrix, the agent cluster time-varying formation control is performed to complete the agent cluster time-varying formation control considering disturbance rejection and time delay.

[0030] like Figure 2As shown, a time-varying formation control method for intelligent agent clusters considering disturbance rejection and latency in this embodiment can be executed by computing devices deployed on a lead intelligent agent, ground control station, or cloud server in the cluster, and specifically includes the following steps: Step S01: Establish an intelligent agent cluster system model that includes nonlinearity, external disturbances, and time-varying delays; Consider a A cluster of intelligent agents, whose directed communication topology is represented by a graph. It means that, among them For a set of nodes, For edge set, This is a weighted adjacency matrix. (Assume a graph.) It contains a spanning tree. The corresponding Laplacian matrix is... ,in Let be the in-degree matrix.

[0031] The dynamics of each agent can be described as follows: (1) in, State (such as position, velocity). To control the input, This is due to external interference. Given a matrix, and assume It can be calmed down. The ranks are full.

[0032] nonlinear functions Satisfying the following one-sided Lipschitz condition and quadratic inner bound constraint (for any vector) , and scalar ): (2) (3) Conditions (2) and (3) are different from the traditional Lipschitz conditions. More generally, allow It is negative and can describe nonlinear systems with larger Lipschitz constants.

[0033] The desired time-varying formation configuration is set as follows: The formation reference trajectory is The goal of time-varying formation control is to design distributed control laws. This makes it possible for all intelligent agents ,when Sometimes, That is, all agents maintain a relative offset from the reference trajectory. At the same time, jointly track .

[0034] Step S02: Design a distributed time-varying formation control protocol; Based on local neighbor information, the following composite control protocol is designed: (4) (5) (6) in, It is the feedback gain matrix to be designed; It is a compensation term used to accurately track the formation reference trajectory, and its specific form is determined by the formation feasibility conditions; It is a coupling strength parameter; It is a time-varying communication delay that satisfies , ,in and It is a known positive constant.

[0035] The protocol includes delayed status feedback. and delayed state differential feedback The aim is to use the delay information and changing trends of neighbors to compensate for the delay impact.

[0036] Step S03: Construct a formation tracking error system; Definition of the first i The formation tracking error of each agent is: (7) make (For the sake of simplifying the analysis, appropriate transformations have been made here; the core is to handle the nonlinear terms in the error dynamics.) , .

[0037] Substitute control protocols (4), (5), and (6) into system (1) and utilize the properties of the Laplace matrix. Thus, the compact form of the closed-loop error system can be obtained: (8) Among them, the relationship with The relevant parts, this part is achieved through reasonable selection It can be made to asymptotically approach zero, which is a feasible condition for realizing time-varying formations. Specific derivation shows that... and , It is related to its derivatives and nonlinear functions, which can be satisfied through design.

[0038] To analyze the consistency of the formation, this embodiment defines the formation error. ,in Let be a preset matrix. Then there is ,in By introducing a non-singular transformation matrix (its specific structure and) (related to the characteristic structure), definition The system (8) can be transformed into an equivalent system with a lower dimension, which is similar in form to (8), but with a reduced matrix dimension, making it easier to analyze: (9) (10) in, and It is the matrix after transformation. and These are the corresponding nonlinear and interference term vectors.

[0039] Step S04: Robustness analysis based on Lyapunov-Krasovskii functionals and linear matrix inequalities; For error systems (9) and (10), a robust time-varying formation control objective is defined: 1) When there is no interference ( The system is asymptotically stable, that is... ; 2) When there is interference, under zero initial conditions, for a given interference suppression level ,satisfy Performance metrics: .

[0040] Therefore, the following augmented Lyapunov-Krasovskii functional is constructed: (11) in, It is an undetermined matrix. It is a state of inclusion Delayed state , and their integral terms over the interval (e.g.) The vector function of (and its quadratic integral term) fully explores the information of state changes within the delay interval.

[0041] calculate Derivative along the trajectory of system (9) In the derivation process, it is necessary to deal with the integral part of the derivative term, such as... To obtain a tighter (less conservative) estimate, this embodiment applies the generalized free matrix integral inequality (lemma) to handle these integral terms and introduces the free matrix. To increase flexibility.

[0042] Using the one-sided Lipschitz condition (4) and the quadratic inner bound condition (5), by introducing a scalar , The influence of nonlinear terms can be expressed in the form of matrix inequalities.

[0043] To handle time-varying delays The system equation (9) is introduced into the derivative in the form of a zero equality, that is, for a matrix of any suitable dimension... ,have: (12) This is equivalent to adding system dynamics constraints to the derivative, thereby reducing conservatism. Combined with... Performance indicators ,available The condition. This condition is ultimately expressed as a condition about and Quadratic function matrix inequalities .

[0044] because In the interval To ensure the inequality holds throughout the entire interval, a relaxed negative definite lemma for quadratic functions is applied. This lemma introduces an adjustable parameter. , will the interval Divided into and And check the endpoints and interior points of the interval. The conditions at the given point are used to transform the matrix inequality conditions on the time-varying parameters into a finite set of matrix inequality conditions.

[0045] Through the above series of derivations and transformations, we obtain the robustness analysis theorem, namely: Assume the system satisfies unilateral Lipschitz conditions, etc. For a given scalar... positive scalar and settings If a symmetric positive definite matrix exists and matrices of any suitable dimension. This makes the following four nonlinear matrix inequalities applicable to... Established: (13) (14) (15) in, It is a system matrix and a gain matrix Lyapunov matrix Free matrix and scalar parameters Complex matrix functions constituted; It is the intermediate matrix or matrix block defined in the derivation; the symbol * represents the symmetric block, then the error system (9) is asymptotically stable and satisfies Performance indicators In other words, intelligent agent swarms can achieve robust time-varying formation control.

[0046] Step S05: Transform the nonlinear matrix inequality into a linear matrix inequality and solve for the controller gain; Inequalities (13), (14), and (15) in the robustness analysis theorem are nonlinear matrix inequalities because they contain the controller gain matrix to be determined. (or ) and Lyapunov matrix The product term. To enable numerical solutions, linearization is required.

[0047] Define a new nonsingular matrix (its dimensions and) (matching the row numbers), and let Perform congruent transformations on the NLMI in the robustness analysis theorem: using block diagonal matrices respectively. , Multiplying each inequality by its left and right sides, where Indicates by A block diagonal matrix of appropriate dimensions, consisting of the Kronecker product and the identity matrix. Simultaneously, define... , , , and order , , ,in It is the scalar parameter to be adjusted.

[0048] After the above complex transformations, inequalities (13), (14), and (15) can be transformed into expressions concerning the new variable. and fixed scalar Linear matrix inequalities.

[0049] This leads to the controller design theorem, namely: For a given scalar positive scalar If a symmetric positive definite matrix exists and matrices of any suitable dimension. This makes the following LMIs true: (16) (17) (18) in, yes The linear form after variable substitution. Therefore, the robust time-varying formation control problem is solvable, and the controller gain is: (19) The feasibility of this set of LMIs was verified and the variables were solved using a convex optimization solver. and The controller gain is then calculated using formula (19).

[0050] Step S06: Optimize controller performance based on particle swarm optimization (PSO) algorithm; In the controller design theorem (i.e., inequalities (16), (17) and (18)), scalar parameters These are free parameters, and their different values ​​will affect the feasible region of LMI, thus affecting the controller gain obtained in the final solution. To achieve optimal robustness, this embodiment introduces a particle swarm optimization algorithm to search for the optimal performance. Parameter combinations.

[0051] In this embodiment, the PSO optimization process is as follows: a. Initialization Set particle swarm size Maximum number of iterations Particle position (i.e.) and the upper and lower limits of speed. , Learning factor Inertial weight Randomly initialize the position of each particle. and speed .

[0052] b. Assess fitness For each particle Current location (i.e., a group) The value is then substituted into the LMI of the controller design theorem. If the LMI is feasible, the corresponding controller gain is obtained. Then, substitute this gain into the closed-loop system for simulation (or calculate based on the performance index function) to obtain a performance evaluation value. .

[0053] This embodiment uses the integral sum of squares of the formation tracking error as the fitness function: (20) in, It is the error between the geometric center of the formation and the reference trajectory; The smaller the value, the better the performance.

[0054] c. Update individual and group optimal Record the best position in the history of each particle (corresponding to the smallest) (value) and the historical best position of the entire group .

[0055] d. Iterative update Based on the PSO velocity and position update formula: (twenty one) (twenty two) in, yes Random numbers within an interval; boundary processing for velocity and position.

[0056] e. Termination judgment Repeat steps b through d until the maximum number of iterations is reached. Or the fitness value meets the accuracy requirements.

[0057] f. Output Output the global optimal position (i.e., optimal parameters) ), corresponding global optimal controller gain and optimal performance value .

[0058] Finally, the optimized gain is used. To construct a control protocol (6) for actual time-varying formation control of intelligent agent clusters.

[0059] To verify the effectiveness of this embodiment, the following simulation experiment was conducted: Consider a swarm of 6 unmanned autonomous aerial vehicles (UAVs) for outer-loop position control, i.e.: (twenty three) The nonlinear function is It satisfies the Lipschitz condition (a special case of OSL). The Laplace matrix corresponding to the communication topology is known.

[0060] set up Performance parameters Applying the corollary of this embodiment (a special case of the controller design theorem under Lipschitz nonlinearity), the maximum allowable delay is calculated when there is a constant time delay. This is significantly longer than the 0.102 seconds of existing methods. More importantly, even with increased performance requirements (i.e., reduced...) The upper limit of the latency that this embodiment can tolerate is still much higher than 0.102 seconds. For example, when It can still handle it even when reduced to 1.6. The delay of a second fully demonstrates the superiority of this embodiment in resisting latency.

[0061] Set the time-varying formation as follows Figure 3 The hexagon is rotated as shown, and a periodic external perturbation is applied. With a large delay... The controller gain designed using the method in this embodiment is simulated in seconds. Figure 6 , Figure 7 , Figure 8 and Figure 9 A snapshot of the cluster motion trajectory using the controller method of this embodiment is shown. Under the same conditions of large delay and disturbance, the geometric center trajectory of the cluster can quickly track the reference trajectory, and after the disturbance ends, it successfully forms a trajectory as shown in the image. Figure 4 The time-varying rotating hexagonal formation shown is as follows: Figure 5 The magnified view shown clearly demonstrates The hexagonal configuration at time points. Results show that the controller designed in this embodiment is highly robust and can achieve accurate time-varying formation tracking even with greater communication delays.

[0062] To further demonstrate the ability of this embodiment to handle more general nonlinearities, consider a more complex OSL nonlinearity: And select the appropriate OSL parameters (such as...) Applying the controller design theorem of this embodiment, the controller design theorem can be calculated at different interference suppression levels. Below, the upper bound of the maximum latency that the system can tolerate. This indicates that the method in this embodiment can effectively handle OSL nonlinear systems, which have a wider range of applications than Lipschitz nonlinearity.

[0063] For the aforementioned unmanned autonomous aerial vehicle swarm model, a delay is set. Seconds, perform PSO optimization; optimization parameters are: 1, 2, 3. The fitness function is the integral of the tracking error. After optimization, the optimal parameter combination and the corresponding controller gain are obtained; Figure 10The evolution curve of the global optimal fitness value during the PSO iteration process shown illustrates its convergence process; as shown... Figure 11 As shown, the comparison is made before optimization (using the unoptimized version). The obtained controller and the optimized formation center tracking error curves show that the optimized controller (red solid line) has a smaller overshoot and a similar or faster convergence speed compared to the unoptimized controller (black dashed line), and the overall tracking performance is improved, proving the effectiveness of PSO optimization in enhancing the robustness of the controller.

[0064] Step S07: Applied to intelligent connected vehicle platooning.

[0065] In practical intelligent connected vehicle platooning, the method provided in this embodiment can achieve robust and adaptive platooning control, addressing vehicle nonlinear dynamics, communication time-varying delays, and external traffic disturbances. The following detailed description, in conjunction with specific implementation methods and hardware systems, further illustrates this: Consider a platooning system consisting of 6 intelligent connected vehicles. Each vehicle can be modeled as a nonlinear system as shown in equation (1), where the state vector... , respectively representing the first The vehicle's position, speed, and acceleration. Control inputs can be commands from the vehicle's drive / braking system. Nonlinear functions can describe nonlinear characteristics such as vehicle aerodynamic drag and tire forces. These nonlinearities generally satisfy the one-sided Lipschitz condition and are suitable for the modeling scope of this embodiment.

[0066] The communication topology is built on a V2V (vehicle-to-vehicle) network and features time-varying latency. The platooning reference trajectory is set as the trajectory of the lead vehicle or a virtual reference vehicle. The time-varying platooning offset can be dynamically adjusted according to traffic flow conditions, supporting various cooperative behaviors such as following, lane changing, and merging in vehicle platooning. The hardware system consists of an embedded controller (ARM Cortex-A series) in each vehicle's onboard control unit, responsible for running the control algorithm described in this embodiment and calculating the control input in real time. The communication module uses DSRC, C-V2X, or 5G communication modules to broadcast and receive status information between vehicles; the sensing system includes GPS / IMU positioning units, radar, cameras, etc., to acquire information about the vehicle's own status and local environment, and some sensor data can be used for interference observation and compensation; the actuators include drive motors, electronic throttles, electronic braking systems, etc., and receive... It issues commands and implements longitudinal / lateral control of vehicles; the formation management unit is installed in the lead vehicle and is responsible for generating and issuing formation reference trajectories and time-varying formation configurations.

[0067] By applying the control protocol (6) designed in this embodiment and the optimized controller gain, vehicles can achieve distributed formation tracking based on local neighbor information (the state of the vehicle in front, the communication delay state). Under the influence of external interference (lateral wind disturbance, changes in road slope), the system can still maintain the preset time-varying formation configuration and meet the given interference suppression performance indicators. .

[0068] To verify the practical effectiveness of this method, Carsim / Simulink co-simulation and hardware-in-the-loop testing were conducted. The hardware-in-the-loop testing platform included: 1. Real-time simulators (dSPACE, NI PXI) for running vehicle dynamics models and traffic scenarios; 2. The actual vehicle-mounted controller hardware is connected to the simulator and executes the control algorithm in real time; 3. A network simulator (Apposite Technologies device) used to inject adjustable time-varying communication latency and packet loss; 4. The disturbance simulation module can add disturbances such as wind resistance changes and sudden changes in road surface adhesion coefficient to the simulation environment.

[0069] In scenarios such as fleet coordination, the method is compared with a fixed-gain controller. Experimental results show that, under the same communication delay and interference levels, the method in this embodiment can support a larger upper limit of delay and exhibits better tracking accuracy and formation stability. In summary, this embodiment provides a robust and highly adaptable distributed time-varying formation control solution for intelligent connected vehicle platooning. The supporting hardware system can realize the entire process verification from algorithm simulation to real vehicle deployment, which can effectively improve fleet driving safety, traffic efficiency, and energy economy.

[0070] This embodiment effectively solves the formation control problem of multi-agent systems with large-scale time-varying communication delays, external interference, and complex nonlinearities by using a one-sided Lipschitz nonlinear model, designing a composite anti-delay control protocol, constructing an advanced Lyapunov functional for analysis, and integrating particle swarm optimization algorithm for parameter optimization. It significantly improves the robustness, anti-interference ability, and engineering practicality of the system.

[0071] Example 2 Embodiment 2 of the present invention introduces a time-varying formation control system for intelligent agent clusters that considers anti-disturbance and anti-delay.

[0072] like Figure 12 The illustrated intelligent agent swarm time-varying formation control system considering disturbance rejection and time delay resistance includes: The acquisition module is configured to acquire the dynamic model of a single agent and build an agent cluster queue. The computing module is configured to compute the composite control protocol of the constructed agent cluster queue based on the agent cluster queue. The building module is configured to construct the tracking error of the time-varying formation of the agent cluster based on the obtained composite control protocol; The analysis module is configured to analyze the constructed tracking error to obtain the feedback gain matrix for time-varying formation control; The optimization module is configured with the parameters of the feedback gain matrix obtained from the optimization iteration to obtain the optimal feedback gain matrix. The control module is configured to perform time-varying formation control of the agent cluster based on the obtained optimal feedback gain matrix, thus completing time-varying formation control of the agent cluster considering disturbance rejection and time delay resistance.

[0073] The detailed steps are the same as those of the time-varying formation control method for intelligent agent clusters that takes into account anti-disturbance and anti-delay provided in Example 1, and will not be repeated here.

[0074] Example 3 Embodiment 3 of the present invention provides a computer-readable storage medium.

[0075] A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of the time-varying formation control method for intelligent agent clusters considering disturbance rejection and latency as described in Embodiment 1 of the present invention.

[0076] The detailed steps are the same as those of the time-varying formation control method for intelligent agent clusters that takes into account anti-disturbance and anti-delay provided in Example 1, and will not be repeated here.

[0077] Example 4 Embodiment 4 of the present invention provides an electronic device.

[0078] An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, it implements the steps in the time-varying formation control method for intelligent agent clusters that considers anti-disturbance and anti-delay as described in Embodiment 1 of the present invention.

[0079] The detailed steps are the same as those of the time-varying formation control method for intelligent agent clusters that takes into account anti-disturbance and anti-delay provided in Example 1, and will not be repeated here.

[0080] Example 5 Embodiment 5 of the present invention provides a computer program product.

[0081] A computer program product includes software code, wherein the program in the software code performs the steps of the time-varying formation control method for intelligent agent clusters considering disturbance rejection and time delay as described in Embodiment 1 of the present invention.

[0082] The detailed steps are the same as those of the time-varying formation control method for intelligent agent clusters that takes into account anti-disturbance and anti-delay provided in Example 1, and will not be repeated here.

[0083] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0084] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0085] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0086] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0087] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0088] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0089] The above description is merely a preferred embodiment of this practice and is not intended to limit the scope of this practice. Various modifications and variations can be made to this practice by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this practice should be included within the protection scope of this practice.

Claims

1. A time-varying formation control method for intelligent agent swarms considering disturbance rejection and delay resistance, characterized in that, include: Obtain the dynamic model of a single agent and construct an agent cluster queue; Based on the constructed agent cluster queue, compute the composite control protocol of the agent cluster queue; Based on the obtained composite control protocol, the tracking error of the time-varying formation of the intelligent agent cluster is constructed; By analyzing the constructed tracking error, the feedback gain matrix of time-varying formation control is obtained; The optimal feedback gain matrix is ​​obtained by optimizing the parameters of the feedback gain matrix obtained through iteration. Based on the obtained optimal feedback gain matrix, the agent cluster time-varying formation control is performed to complete the agent cluster time-varying formation control considering disturbance rejection and time delay.

2. The time-varying formation control method for intelligent agent clusters considering disturbance rejection and time delay resistance as described in claim 1, characterized in that, The obtained dynamic model of a single agent is ;in, For the first The state vector of each agent. For the first The control input vector of an agent. To act on the first External interference to an intelligent agent; These are constant matrices with appropriate dimensions; It is a nonlinear function that satisfies the one-sided Lipschitz condition and the quadratic inner bound constraint.

3. The time-varying formation control method for intelligent agent clusters considering disturbance rejection and time delay resistance as described in claim 2, characterized in that, The resulting composite control protocol for the agent cluster queue is ;in, For the first The offset vector of each agent in the desired formation For formation reference trajectory, For time-varying communication delay and satisfying , The feedback gain matrix to be designed, For coupling strength parameters, For formation tracking compensation function, These are elements of the communication topology adjacency matrix.

4. The time-varying formation control method for intelligent agent clusters considering disturbance rejection and time delay resistance as described in claim 3, characterized in that, No. i The formation tracking error of each agent is ;make , , ; Substituting the composite control protocol into the dynamic model and combining it with the properties of the Laplace matrix This yields a compact form of the closed-loop error system. .

5. The time-varying formation control method for intelligent agent clusters considering disturbance rejection and time delay resistance as described in claim 1, characterized in that, Robustness analysis of the tracking error is conducted using Lyapunov-Krasovskii functionals and linear matrix inequalities. Specifically, an augmented Lyapunov-Krasovskii functional is constructed, and combined with a novel generalized free matrix integral inequality for handling integral terms and a relaxed negative definite lemma for quadratic functions, a derive is made that allows the formation tracking error system to satisfy predetermined conditions under the presence of external disturbances and time-varying delays. Sufficient conditions for interference suppression performance; the sufficient conditions are expressed as a set of nonlinear matrix inequalities with respect to the controller gain matrix and the functional correlation matrix.

6. The time-varying formation control method for intelligent agent clusters considering disturbance rejection and time delay resistance as described in claim 1, characterized in that, The obtained feedback gain matrix parameters are optimized iteratively using the particle swarm optimization algorithm. The feedback gain matrix parameters are used as optimization variables, and the integral performance index of the formation tracking error is used as the fitness function. The parameter combination is iteratively searched through the particle swarm optimization algorithm to find the parameter combination that makes the tracking performance optimal, and thus the optimal feedback gain matrix is ​​obtained.

7. A time-varying formation control system for intelligent agent swarms considering disturbance rejection and time delay resistance, characterized in that, include: The acquisition module is configured to acquire the dynamic model of a single agent and build an agent cluster queue. The computing module is configured to compute the composite control protocol of the constructed agent cluster queue. The building module is configured to construct the tracking error of the time-varying formation of the agent cluster based on the obtained composite control protocol; The analysis module is configured to analyze the constructed tracking error to obtain the feedback gain matrix for time-varying formation control; The optimization module is configured with the parameters of the feedback gain matrix obtained from the optimization iteration to obtain the optimal feedback gain matrix. The control module is configured to perform time-varying formation control of the agent cluster based on the obtained optimal feedback gain matrix, thus completing time-varying formation control of the agent cluster considering disturbance rejection and time delay resistance.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the time-varying formation control method for intelligent agent clusters that takes into account disturbance rejection and latency as described in any one of claims 1-6.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the program, it implements the steps of the time-varying formation control method for intelligent agent clusters that considers anti-disturbance and anti-delay as described in any one of claims 1-6.

10. A computer program product, comprising software code, characterized in that, The program in the software code executes the steps of the time-varying formation control method for intelligent agent clusters that considers anti-disturbance and anti-delay as described in any one of claims 1-6.

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