This paper delves into the problem of optimal placement conditions for a group of agents collaboratively localizing a target using range-only or bearing-only measurements.The challenge in this study stems from the unc...This paper delves into the problem of optimal placement conditions for a group of agents collaboratively localizing a target using range-only or bearing-only measurements.The challenge in this study stems from the uncertainty associated with the positions of the agents,which may experience drift or disturbances during the target localization process.Initially,we derive the Cramer-Rao lower bound(CRLB)of the target position as the primary analytical metric.Subsequently,we establish the necessary and sufficient conditions for the optimal placement of agents.Based on these conditions,we analyze the maximal allowable agent position error for an expected mean squared error(MSE),providing valuable guidance for the selection of agent positioning sensors.The analytical findings are further validated through simulation experiments.展开更多
Aiming for the coordinated motion and cooperative control of multi-agents in a non-rectangular bounded space, a velocity consensus algorithm for the agents with double- integrator dynamics is presented. The traditiona...Aiming for the coordinated motion and cooperative control of multi-agents in a non-rectangular bounded space, a velocity consensus algorithm for the agents with double- integrator dynamics is presented. The traditional consensus algorithm for bounded space is only applicable to rectangular bouncing boundaries, not suitable for non-rectangular space. In order to extend the previous consensus algorithm to the non- rectangular space, the concept of mirrored velocity is introduced, which can convert the discontinuous real velocity to continuous mirrored velocity, and expand a bounded space into an infinite space. Using the consensus algorithm, it is found that the mirrored velocities of multi-agents asymptotically converge to the same values. Because each mirrored velocity points to a unique velocity in real space, it can be concluded that the real velocities of multi-agents also asymptotically converge. Finally, the effectiveness of the proposed consensus algorithm is examined by theoretical proof and numerical simulations. Moreover, an experiment is performed with the algorithm in a real multi-robot system successfully.展开更多
This paper discusses the progress of computer integrated processing （CIPS） of coal-preparation and then preserits an intelligence controlled production-process, device-maintenance and production-management system of...This paper discusses the progress of computer integrated processing （CIPS） of coal-preparation and then preserits an intelligence controlled production-process, device-maintenance and production-management system of coal- preparation based on multi-agents （IICMMS-CP）. The construction of the IICMMS-CP, the distributed network control system based on live intelligence control stations and the strategy of implementing distributed intelligence control system are studied in order to overcome the disadvantages brought about by the wide use of the PLC system by coaipreparation plants. The software frame, based on a Multi-Agent Intelligence Control and Maintenance Management integrated system, is studied and the implemention methods of IICMMS-CP are discussed. The characteristics of distributed architecture, cooperation and parallel computing meet the needs of integrated control of coal-preparation plants with large-scale spatial production distribution, densely-related processes and complex systems. Its application further improves the reliability and precision of process control, accuracy of fault identification and intelligence of production adjustment, establishes a technical basis for system integration and flexible production. The main function of the system has been tested in a coal-preparation plant to good effect in stabilizing product quality, improving efficiency and reducing consumption.展开更多
This paper researches some problems in complex formation for multi-agents,in which two matrices are proposed to record the formation.The pattern matrix is used to describe the pattern of the formation;meanwhile,the lo...This paper researches some problems in complex formation for multi-agents,in which two matrices are proposed to record the formation.The pattern matrix is used to describe the pattern of the formation;meanwhile,the location matrix is used to record the location of each agent.Thus,all desired positions of each agent will be obtained by geometrical relationship on the basis of two matrices above.In addition a self-adaptation flocking algorithm is proposed to control all agents to form a desired formation and avoid obstacles.The main idea is as follows:agents will form a desired formation through the method of formation control when far away from obstacles;otherwise,agents will freely fly to pass through the area of obstacles.In the simulation,three scenarios are designed to verify the effectiveness of our method.The results show that our method also can be applied in three dimensions.All agents will form a stable formation and keep the same velocity at last.展开更多
In this paper,the bipartite consensus problem is studied for a class of uncertain high-order nonlinear multi-agent systems.A signed digraph is presented to describe the collaborative and competitive interactions among...In this paper,the bipartite consensus problem is studied for a class of uncertain high-order nonlinear multi-agent systems.A signed digraph is presented to describe the collaborative and competitive interactions among agents.For each agent with lower triangular structure,a time-varying gain compensator is first designed by relative output information of neighboring agents.Subsequently,a distributed controller with dynamic event-triggered mechanism is proposed to drive the bipartite consensus error to zero.It is worth noting that an internal dynamic variable is introduced in triggering function,which plays an essential role in excluding the Zeno behavior and reducing energy consumption.Furthermore,the dynamic event-triggered control protocol is developed for upper triangular multi-agent systems to realize the bipartite consensus without Zeno behavior.Finally,simulation examples are provided to illustrate the effectiveness of the presented results.展开更多
In these latter days software agents are used for the development and implementation of intellectual decision support systems. In order to implement intelligence in a system some or several dozen of software agents ar...In these latter days software agents are used for the development and implementation of intellectual decision support systems. In order to implement intelligence in a system some or several dozen of software agents are used and the made system becomes multi-agent. For the development of these systems a set of methodologies, i.e., the sequence of consequent steps of analysis, designing and implementation, is offered. The carried out analysis of the methodologies showed that as a rule they are limited by the spectrum of their pending problem （within the pales of the requirements of specific applied task, within the pales of the possibilities of technical implementation） or within the pales of amount of detail. The variety of methodologies is influenced by the fact that for the development of these systems the requirements and attitudes are offered by the specialists of related spheres such as software, numeral intellect engineers. In the course of the development of hardware and software appeared possibilities to implement mobile multi-agents systems, however, there is no one united mobile multi-agent systems design methodology, whereas existing systems are underdeveloped and their number is small. In this article we introduce the course of the designing of an intellectual real time multi-agent investment management decision support information system adapting and combining some methodologies where the choice to use either communicating or mobile agents is the question of rather technical implementation than methodological. In the article we introduce two ways of system implementation by JADE platform： the first one-using communicating agents, and the second one-using mobile agents.展开更多
This paper deals with the distributed consensus problem of high-order multi-agent systems with nonlinear dynamics subject to external disturbances. The network topology is assumed to be a fixed undirected graph. Some ...This paper deals with the distributed consensus problem of high-order multi-agent systems with nonlinear dynamics subject to external disturbances. The network topology is assumed to be a fixed undirected graph. Some sufficient conditions are derived, under which the consensus can be achieved with a prescribed norm bound. It is shown that the parameter matrix in the consensus algorithm can be designed by solving two linear matrix inequalities (LMIs). In particular, if the nonzero eigenvalues of the laplacian matrix ac-cording to the network topology are identical, the parameter matrix in the consensus algorithm can be de-signed by solving one LMI. A numerical example is given to illustrate the proposed results.展开更多
This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary obj...This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary objective is to explore the unknown environments to locate and track targets effectively. To address this problem, we propose a novel Multi-Agent Reinforcement Learning (MARL) method based on Graph Neural Network (GNN). Firstly, a method is introduced for encoding continuous-space multi-UAV problem data into spatial graphs which establish essential relationships among agents, obstacles, and targets. Secondly, a Graph AttenTion network (GAT) model is presented, which focuses exclusively on adjacent nodes, learns attention weights adaptively and allows agents to better process information in dynamic environments. Reward functions are specifically designed to tackle exploration challenges in environments with sparse rewards. By introducing a framework that integrates centralized training and distributed execution, the advancement of models is facilitated. Simulation results show that the proposed method outperforms the existing MARL method in search rate and tracking performance with less collisions. The experiments show that the proposed method can be extended to applications with a larger number of agents, which provides a potential solution to the challenging problem of multi-UAV autonomous tracking in dynamic unknown environments.展开更多
This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method...This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.展开更多
Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-...Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-robot control.Empowering cooperative MARL with multi-task decision-making capabilities is expected to further broaden its application scope.In multi-task scenarios,cooperative MARL algorithms need to address 3 types of multi-task problems:reward-related multi-task,arising from different reward functions;multi-domain multi-task,caused by differences in state and action spaces,state transition functions;and scalability-related multi-task,resulting from the dynamic variation in the number of agents.Most existing studies focus on scalability-related multitask problems.However,with the increasing integration between large language models(LLMs)and multi-agent systems,a growing number of LLM-based multi-agent systems have emerged,enabling more complex multi-task cooperation.This paper provides a comprehensive review of the latest advances in this field.By combining multi-task reinforcement learning with cooperative MARL,we categorize and analyze the 3 major types of multi-task problems under multi-agent settings,offering more fine-grained classifications and summarizing key insights for each.In addition,we summarize commonly used benchmarks and discuss future directions of research in this area,which hold promise for further enhancing the multi-task cooperation capabilities of multi-agent systems and expanding their practical applications in the real world.展开更多
The development of chassis active safety control technology has improved vehicle stability under extreme conditions.However,its cross-system and multi-functional characteristics make the controller difficult to achiev...The development of chassis active safety control technology has improved vehicle stability under extreme conditions.However,its cross-system and multi-functional characteristics make the controller difficult to achieve cooperative goals.In addition,the chassis system,which has high complexity,numerous subsystems,and strong coupling,will also lead to low computing efficiency and poor control effect of the controller.Therefore,this paper proposes a scenario-driven hybrid distributed model predictive control algorithm with variable control topology.This algorithm divides multiple stability regions based on the vehicle’s β−γ phase plane,forming a mapping relationship between the control structure and the vehicle’s state.A control input fusion mechanism within the transition domain is designed to mitigate the problems of system state oscillation and control input jitter caused by switching control structures.Then,a distributed state-space equation with state coupling and input coupling characteristics is constructed,and a weighted local agent cost function in quadratic programming is derived.Through cost coupling,local agents can coordinate global performance goals.Finally,through Simulink/CarSim joint simulation and hardware-in-the-loop(HIL)test,the proposed algorithm is validated to improve vehicle stability while ensuring trajectory tracking accuracy and has good applicability for multi-objective coordinated control.This paper combines the advantages of distributed MPC and decentralized MPC,achieving a balance between approximating the global optimal results and the solution’s efficiency.展开更多
Formation control in multi-agent systems has become a critical area of interest due to its wide-ranging applications in robotics,autonomous transportation,and surveillance.While various studies have explored distribut...Formation control in multi-agent systems has become a critical area of interest due to its wide-ranging applications in robotics,autonomous transportation,and surveillance.While various studies have explored distributed cooperative control,this review focuses on the theoretical foundations and recent developments in formation control strategies.The paper categorizes and analyzes key formation types,including formation maintenance,group or cluster formation,bipartite formations,event-triggered formations,finite-time convergence,and constrained formations.A significant portion of the review addresses formation control under constrained dynamics,presenting both modelbased and model-free approaches that consider practical limitations such as actuator bounds,communication delays,and nonholonomic constraints.Additionally,the paper discusses emerging trends,including the integration of eventdriven mechanisms and AI-enhanced coordination strategies.Comparative evaluations highlight the trade-offs among various methodologies regarding scalability,robustness,and real-world feasibility.Practical implementations are reviewed across diverse platforms,and the review identifies the current achievements and unresolved challenges in the field.The paper concludes by outlining promising research directions,such as adaptive control for dynamic environments,energy-efficient coordination,and using learning-based control under uncertainty.This review synthesizes the current state of the art and provides a road map for future investigation,making it a valuable reference for researchers and practitioners aiming to advance formation control in multi-agent systems.展开更多
This paper mainly focuses on the velocity-constrained consensus problem of discrete-time heterogeneous multi-agent systems with nonconvex constraints and arbitrarily switching topologies,where each agent has first-ord...This paper mainly focuses on the velocity-constrained consensus problem of discrete-time heterogeneous multi-agent systems with nonconvex constraints and arbitrarily switching topologies,where each agent has first-order or second-order dynamics.To solve this problem,a distributed algorithm is proposed based on a contraction operator.By employing the properties of the stochastic matrix,it is shown that all agents’position states could converge to a common point and second-order agents’velocity states could remain in corresponding nonconvex constraint sets and converge to zero as long as the joint communication topology has one directed spanning tree.Finally,the numerical simulation results are provided to verify the effectiveness of the proposed algorithms.展开更多
In recent years,significant research attention has been directed towards swarm intelligence.The Milling behavior of fish schools,a prime example of swarm intelligence,shows how simple rules followed by individual agen...In recent years,significant research attention has been directed towards swarm intelligence.The Milling behavior of fish schools,a prime example of swarm intelligence,shows how simple rules followed by individual agents lead to complex collective behaviors.This paper studies Multi-Agent Reinforcement Learning to simulate fish schooling behavior,overcoming the challenges of tuning parameters in traditional models and addressing the limitations of single-agent methods in multi-agent environments.Based on this foundation,a novel Graph Convolutional Networks(GCN)-Critic MADDPG algorithm leveraging GCN is proposed to enhance cooperation among agents in a multi-agent system.Simulation experiments demonstrate that,compared to traditional single-agent algorithms,the proposed method not only exhibits significant advantages in terms of convergence speed and stability but also achieves tighter group formations and more naturally aligned Milling behavior.Additionally,a fish school self-organizing behavior research platform based on an event-triggered mechanism has been developed,providing a robust tool for exploring dynamic behavioral changes under various conditions.展开更多
文摘This paper delves into the problem of optimal placement conditions for a group of agents collaboratively localizing a target using range-only or bearing-only measurements.The challenge in this study stems from the uncertainty associated with the positions of the agents,which may experience drift or disturbances during the target localization process.Initially,we derive the Cramer-Rao lower bound(CRLB)of the target position as the primary analytical metric.Subsequently,we establish the necessary and sufficient conditions for the optimal placement of agents.Based on these conditions,we analyze the maximal allowable agent position error for an expected mean squared error(MSE),providing valuable guidance for the selection of agent positioning sensors.The analytical findings are further validated through simulation experiments.
基金The National Natural Science Foundation of China(No.61273110)the Specialized Fund for the Doctoral Program of Higher Education(No.20130092130002)
文摘Aiming for the coordinated motion and cooperative control of multi-agents in a non-rectangular bounded space, a velocity consensus algorithm for the agents with double- integrator dynamics is presented. The traditional consensus algorithm for bounded space is only applicable to rectangular bouncing boundaries, not suitable for non-rectangular space. In order to extend the previous consensus algorithm to the non- rectangular space, the concept of mirrored velocity is introduced, which can convert the discontinuous real velocity to continuous mirrored velocity, and expand a bounded space into an infinite space. Using the consensus algorithm, it is found that the mirrored velocities of multi-agents asymptotically converge to the same values. Because each mirrored velocity points to a unique velocity in real space, it can be concluded that the real velocities of multi-agents also asymptotically converge. Finally, the effectiveness of the proposed consensus algorithm is examined by theoretical proof and numerical simulations. Moreover, an experiment is performed with the algorithm in a real multi-robot system successfully.
文摘This paper discusses the progress of computer integrated processing （CIPS） of coal-preparation and then preserits an intelligence controlled production-process, device-maintenance and production-management system of coal- preparation based on multi-agents （IICMMS-CP）. The construction of the IICMMS-CP, the distributed network control system based on live intelligence control stations and the strategy of implementing distributed intelligence control system are studied in order to overcome the disadvantages brought about by the wide use of the PLC system by coaipreparation plants. The software frame, based on a Multi-Agent Intelligence Control and Maintenance Management integrated system, is studied and the implemention methods of IICMMS-CP are discussed. The characteristics of distributed architecture, cooperation and parallel computing meet the needs of integrated control of coal-preparation plants with large-scale spatial production distribution, densely-related processes and complex systems. Its application further improves the reliability and precision of process control, accuracy of fault identification and intelligence of production adjustment, establishes a technical basis for system integration and flexible production. The main function of the system has been tested in a coal-preparation plant to good effect in stabilizing product quality, improving efficiency and reducing consumption.
文摘This paper researches some problems in complex formation for multi-agents,in which two matrices are proposed to record the formation.The pattern matrix is used to describe the pattern of the formation;meanwhile,the location matrix is used to record the location of each agent.Thus,all desired positions of each agent will be obtained by geometrical relationship on the basis of two matrices above.In addition a self-adaptation flocking algorithm is proposed to control all agents to form a desired formation and avoid obstacles.The main idea is as follows:agents will form a desired formation through the method of formation control when far away from obstacles;otherwise,agents will freely fly to pass through the area of obstacles.In the simulation,three scenarios are designed to verify the effectiveness of our method.The results show that our method also can be applied in three dimensions.All agents will form a stable formation and keep the same velocity at last.
基金This work was supported by the National Natural Science Foundation of China(Nos.61973189,62073190)the Research Fund for the Taishan Scholar Project of Shandong Province of China(No.ts20190905)the Natural Science Foundation of Shandong Province of China(No.ZR2020ZD25).
文摘In this paper,the bipartite consensus problem is studied for a class of uncertain high-order nonlinear multi-agent systems.A signed digraph is presented to describe the collaborative and competitive interactions among agents.For each agent with lower triangular structure,a time-varying gain compensator is first designed by relative output information of neighboring agents.Subsequently,a distributed controller with dynamic event-triggered mechanism is proposed to drive the bipartite consensus error to zero.It is worth noting that an internal dynamic variable is introduced in triggering function,which plays an essential role in excluding the Zeno behavior and reducing energy consumption.Furthermore,the dynamic event-triggered control protocol is developed for upper triangular multi-agent systems to realize the bipartite consensus without Zeno behavior.Finally,simulation examples are provided to illustrate the effectiveness of the presented results.
文摘In these latter days software agents are used for the development and implementation of intellectual decision support systems. In order to implement intelligence in a system some or several dozen of software agents are used and the made system becomes multi-agent. For the development of these systems a set of methodologies, i.e., the sequence of consequent steps of analysis, designing and implementation, is offered. The carried out analysis of the methodologies showed that as a rule they are limited by the spectrum of their pending problem （within the pales of the requirements of specific applied task, within the pales of the possibilities of technical implementation） or within the pales of amount of detail. The variety of methodologies is influenced by the fact that for the development of these systems the requirements and attitudes are offered by the specialists of related spheres such as software, numeral intellect engineers. In the course of the development of hardware and software appeared possibilities to implement mobile multi-agents systems, however, there is no one united mobile multi-agent systems design methodology, whereas existing systems are underdeveloped and their number is small. In this article we introduce the course of the designing of an intellectual real time multi-agent investment management decision support information system adapting and combining some methodologies where the choice to use either communicating or mobile agents is the question of rather technical implementation than methodological. In the article we introduce two ways of system implementation by JADE platform： the first one-using communicating agents, and the second one-using mobile agents.
文摘This paper deals with the distributed consensus problem of high-order multi-agent systems with nonlinear dynamics subject to external disturbances. The network topology is assumed to be a fixed undirected graph. Some sufficient conditions are derived, under which the consensus can be achieved with a prescribed norm bound. It is shown that the parameter matrix in the consensus algorithm can be designed by solving two linear matrix inequalities (LMIs). In particular, if the nonzero eigenvalues of the laplacian matrix ac-cording to the network topology are identical, the parameter matrix in the consensus algorithm can be de-signed by solving one LMI. A numerical example is given to illustrate the proposed results.
基金supported by the National Natural Science Foundation of China(Nos.12272104,U22B2013).
文摘This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary objective is to explore the unknown environments to locate and track targets effectively. To address this problem, we propose a novel Multi-Agent Reinforcement Learning (MARL) method based on Graph Neural Network (GNN). Firstly, a method is introduced for encoding continuous-space multi-UAV problem data into spatial graphs which establish essential relationships among agents, obstacles, and targets. Secondly, a Graph AttenTion network (GAT) model is presented, which focuses exclusively on adjacent nodes, learns attention weights adaptively and allows agents to better process information in dynamic environments. Reward functions are specifically designed to tackle exploration challenges in environments with sparse rewards. By introducing a framework that integrates centralized training and distributed execution, the advancement of models is facilitated. Simulation results show that the proposed method outperforms the existing MARL method in search rate and tracking performance with less collisions. The experiments show that the proposed method can be extended to applications with a larger number of agents, which provides a potential solution to the challenging problem of multi-UAV autonomous tracking in dynamic unknown environments.
基金The National Natural Science Foundation of China(W2431048)The Science and Technology Research Program of Chongqing Municipal Education Commission,China(KJZDK202300807)The Chongqing Natural Science Foundation,China(CSTB2024NSCQQCXMX0052).
文摘This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.
基金The National Natural Science Foundation of China(62136008,62293541)The Beijing Natural Science Foundation(4232056)The Beijing Nova Program(20240484514).
文摘Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-robot control.Empowering cooperative MARL with multi-task decision-making capabilities is expected to further broaden its application scope.In multi-task scenarios,cooperative MARL algorithms need to address 3 types of multi-task problems:reward-related multi-task,arising from different reward functions;multi-domain multi-task,caused by differences in state and action spaces,state transition functions;and scalability-related multi-task,resulting from the dynamic variation in the number of agents.Most existing studies focus on scalability-related multitask problems.However,with the increasing integration between large language models(LLMs)and multi-agent systems,a growing number of LLM-based multi-agent systems have emerged,enabling more complex multi-task cooperation.This paper provides a comprehensive review of the latest advances in this field.By combining multi-task reinforcement learning with cooperative MARL,we categorize and analyze the 3 major types of multi-task problems under multi-agent settings,offering more fine-grained classifications and summarizing key insights for each.In addition,we summarize commonly used benchmarks and discuss future directions of research in this area,which hold promise for further enhancing the multi-task cooperation capabilities of multi-agent systems and expanding their practical applications in the real world.
基金Supported by National Natural Science Foundation of China(Grant Nos.52225212,52272418,U22A20100)National Key Research and Development Program of China(Grant No.2022YFB2503302).
文摘The development of chassis active safety control technology has improved vehicle stability under extreme conditions.However,its cross-system and multi-functional characteristics make the controller difficult to achieve cooperative goals.In addition,the chassis system,which has high complexity,numerous subsystems,and strong coupling,will also lead to low computing efficiency and poor control effect of the controller.Therefore,this paper proposes a scenario-driven hybrid distributed model predictive control algorithm with variable control topology.This algorithm divides multiple stability regions based on the vehicle’s β−γ phase plane,forming a mapping relationship between the control structure and the vehicle’s state.A control input fusion mechanism within the transition domain is designed to mitigate the problems of system state oscillation and control input jitter caused by switching control structures.Then,a distributed state-space equation with state coupling and input coupling characteristics is constructed,and a weighted local agent cost function in quadratic programming is derived.Through cost coupling,local agents can coordinate global performance goals.Finally,through Simulink/CarSim joint simulation and hardware-in-the-loop(HIL)test,the proposed algorithm is validated to improve vehicle stability while ensuring trajectory tracking accuracy and has good applicability for multi-objective coordinated control.This paper combines the advantages of distributed MPC and decentralized MPC,achieving a balance between approximating the global optimal results and the solution’s efficiency.
基金supported in part by the National Natural Science Foundation of China under Grant 6237319in part by the Postgraduate Research and Practice Innovation Program of Jiangsu Province under Grant KYCX230479.
文摘Formation control in multi-agent systems has become a critical area of interest due to its wide-ranging applications in robotics,autonomous transportation,and surveillance.While various studies have explored distributed cooperative control,this review focuses on the theoretical foundations and recent developments in formation control strategies.The paper categorizes and analyzes key formation types,including formation maintenance,group or cluster formation,bipartite formations,event-triggered formations,finite-time convergence,and constrained formations.A significant portion of the review addresses formation control under constrained dynamics,presenting both modelbased and model-free approaches that consider practical limitations such as actuator bounds,communication delays,and nonholonomic constraints.Additionally,the paper discusses emerging trends,including the integration of eventdriven mechanisms and AI-enhanced coordination strategies.Comparative evaluations highlight the trade-offs among various methodologies regarding scalability,robustness,and real-world feasibility.Practical implementations are reviewed across diverse platforms,and the review identifies the current achievements and unresolved challenges in the field.The paper concludes by outlining promising research directions,such as adaptive control for dynamic environments,energy-efficient coordination,and using learning-based control under uncertainty.This review synthesizes the current state of the art and provides a road map for future investigation,making it a valuable reference for researchers and practitioners aiming to advance formation control in multi-agent systems.
基金2024 Jiangsu Province Youth Science and Technology Talent Support Project2024 Yancheng Key Research and Development Plan(Social Development)projects,“Research and Application of Multi Agent Offline Distributed Trust Perception Virtual Wireless Sensor Network Algorithm”and“Research and Application of a New Type of Fishery Ship Safety Production Monitoring Equipment”。
文摘This paper mainly focuses on the velocity-constrained consensus problem of discrete-time heterogeneous multi-agent systems with nonconvex constraints and arbitrarily switching topologies,where each agent has first-order or second-order dynamics.To solve this problem,a distributed algorithm is proposed based on a contraction operator.By employing the properties of the stochastic matrix,it is shown that all agents’position states could converge to a common point and second-order agents’velocity states could remain in corresponding nonconvex constraint sets and converge to zero as long as the joint communication topology has one directed spanning tree.Finally,the numerical simulation results are provided to verify the effectiveness of the proposed algorithms.
基金supported by the National Natural Science Foundation of China under Grant 62273351 and Grant 62303020.
文摘In recent years,significant research attention has been directed towards swarm intelligence.The Milling behavior of fish schools,a prime example of swarm intelligence,shows how simple rules followed by individual agents lead to complex collective behaviors.This paper studies Multi-Agent Reinforcement Learning to simulate fish schooling behavior,overcoming the challenges of tuning parameters in traditional models and addressing the limitations of single-agent methods in multi-agent environments.Based on this foundation,a novel Graph Convolutional Networks(GCN)-Critic MADDPG algorithm leveraging GCN is proposed to enhance cooperation among agents in a multi-agent system.Simulation experiments demonstrate that,compared to traditional single-agent algorithms,the proposed method not only exhibits significant advantages in terms of convergence speed and stability but also achieves tighter group formations and more naturally aligned Milling behavior.Additionally,a fish school self-organizing behavior research platform based on an event-triggered mechanism has been developed,providing a robust tool for exploring dynamic behavioral changes under various conditions.
