This paper deals with the problem of designing robust sequential covariance intersection(SCI)fusion Kalman filter for the clustering multi-agent sensor network system with measurement delays and uncertain noise varian...This paper deals with the problem of designing robust sequential covariance intersection(SCI)fusion Kalman filter for the clustering multi-agent sensor network system with measurement delays and uncertain noise variances.The sensor network is partitioned into clusters by the nearest neighbor rule.Using the minimax robust estimation principle,based on the worst-case conservative sensor network system with conservative upper bounds of noise variances,and applying the unbiased linear minimum variance(ULMV)optimal estimation rule,we present the two-layer SCI fusion robust steady-state Kalman filter which can reduce communication and computation burdens and save energy sources,and guarantee that the actual filtering error variances have a less-conservative upper-bound.A Lyapunov equation method for robustness analysis is proposed,by which the robustness of the local and fused Kalman filters is proved.The concept of the robust accuracy is presented and the robust accuracy relations of the local and fused robust Kalman filters are proved.It is proved that the robust accuracy of the global SCI fuser is higher than those of the local SCI fusers and the robust accuracies of all SCI fusers are higher than that of each local robust Kalman filter.A simulation example for a tracking system verifies the robustness and robust accuracy relations.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
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
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
Due to the characteristics of line-of-sight(LoS)communication in unmanned aerial vehicle(UAV)networks,these systems are highly susceptible to eavesdropping and surveillance.To effectively address the security concerns...Due to the characteristics of line-of-sight(LoS)communication in unmanned aerial vehicle(UAV)networks,these systems are highly susceptible to eavesdropping and surveillance.To effectively address the security concerns in UAV communication,covert communication methods have been adopted.This paper explores the joint optimization problem of trajectory and transmission power in a multi-hop UAV relay covert communication system.Considering the communication covertness,power constraints,and trajectory limitations,an algorithm based on multi-agent proximal policy optimization(MAPPO),named covert-MAPPO(C-MAPPO),is proposed.The proposed method leverages the strengths of both optimization algorithms and reinforcement learning to analyze and make joint decisions on the transmission power and flight trajectory strategies for UAVs to achieve cooperation.Simulation results demonstrate that the proposed method can maximize the system throughput while satisfying covertness constraints,and it outperforms benchmark algorithms in terms of system throughput and reward convergence speed.展开更多
Multi-agent systems(MASs)have demonstrated significant achievements in a wide range of tasks,leveraging their capacity for coordination and adaptation within complex environments.Moreover,the enhancement of their inte...Multi-agent systems(MASs)have demonstrated significant achievements in a wide range of tasks,leveraging their capacity for coordination and adaptation within complex environments.Moreover,the enhancement of their intelligent functionalities is crucial for tackling increasingly challenging tasks.This goal resonates with a paradigm shift within the artificial intelligence(AI)community,from“internet AI”to“embodied AI”,and the MASs with embodied AI are referred to as embodied multi-agent systems(EMASs).An EMAS has the potential to acquire generalized competencies through interactions with environments,enabling it to effectively address a variety of tasks and thereby make a substantial contribution to the quest for artificial general intelligence.Despite the burgeoning interest in this domain,a comprehensive review of EMAS has been lacking.This paper offers analysis and synthesis for EMASs from a control perspective,conceptualizing each embodied agent as an entity equipped with a“brain”for decision and a“body”for environmental interaction.System designs are classified into open-loop,closed-loop,and double-loop categories,and EMAS implementations are discussed.Additionally,the current applications and challenges faced by EMASs are summarized and potential avenues for future research in this field are provided.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
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
This paper focuses on the problem of leaderfollowing consensus for nonlinear cascaded multi-agent systems.The control strategies for these systems are transformed into successive control problem schemes for lower-orde...This paper focuses on the problem of leaderfollowing consensus for nonlinear cascaded multi-agent systems.The control strategies for these systems are transformed into successive control problem schemes for lower-order error subsystems.A distributed consensus analysis for the corresponding error systems is conducted by employing recursive methods and virtual controllers,accompanied by a series of Lyapunov functions devised throughout the iterative process,which solves the leaderfollowing consensus problem of a class of nonlinear cascaded multi-agent systems.Specific simulation examples illustrate the effectiveness of the proposed control algorithm.展开更多
In this paper,the distributed optimal formation control problem of heterogeneous Euler–Lagrange multi-agent systems with generic formation constraints and inequality constraints is investigated.Based on the primal–d...In this paper,the distributed optimal formation control problem of heterogeneous Euler–Lagrange multi-agent systems with generic formation constraints and inequality constraints is investigated.Based on the primal–dual dynamics and the adaptive control technique,a distributed optimal formation controller consists of a velocity reference signal generator and a velocity tracking controller is proposed.By using the optimality condition,the relationship between the equilibrium point of the closed-loop system and the optimal solution of the optimization problem is established.Then,by utilizing Lyapunov stability analysis,it is rigorously proved that the optimal formation is reached with the proposed controller.Lastly,simulation examples are provided to substantiate the theoretical results.展开更多
This article investigates the time-varying output group formation tracking control(GFTC)problem for heterogeneous multi-agent systems(HMASs)under switching topologies.The objective is to design a distributed control s...This article investigates the time-varying output group formation tracking control(GFTC)problem for heterogeneous multi-agent systems(HMASs)under switching topologies.The objective is to design a distributed control strategy that enables the outputs of the followers to form the desired sub-formations and track the outputs of the leader in each subgroup.Firstly,novel distributed observers are developed to estimate the states of the leaders under switching topologies.Then,GFTC protocols are designed based on the proposed observers.It is shown that with the distributed protocol,the GFTC problem for HMASs under switching topologies is solved if the average dwell time associated with the switching topologies is larger than a fixed threshold.Finally,an example is provided to illustrate the effectiveness of the proposed control strategy.展开更多
When a fire breaks out in a high-rise building,the occlusion of smoke and obstacles results in dearth of crucial information concerning people in distress,thereby creating a challenge in their detection.Given the rest...When a fire breaks out in a high-rise building,the occlusion of smoke and obstacles results in dearth of crucial information concerning people in distress,thereby creating a challenge in their detection.Given the restricted sensing range of a single unmanned aerial vehicle(UAV)cam-era,enhancing the target recognition rate becomes challenging without target information.To tackle this issue,this paper proposes a multi-agent autonomous collaborative detection method for multi-targets in complex fire environments.The objective is to achieve the fusion of multi-angle visual information,effectively increasing the target’s information dimension,and ultimately address-ing the problem of low target recognition rate caused by the lack of target information.The method steps are as follows:first,the you only look once version5(YOLOv5)is used to detect the target in the image;second,the detected targets are tracked to monitor their movements and trajectories;third,the person re-identification(ReID)model is employed to extract the appearance features of targets;finally,by fusing the visual information from multi-angle cameras,the method achieves multi-agent autonomous collaborative detection.The experimental results show that the method effectively combines the visual information from multi-angle cameras,resulting in improved detec-tion efficiency for people in distress.展开更多
This paper proposes a Multi-Agent Attention Proximal Policy Optimization(MA2PPO)algorithm aiming at the problems such as credit assignment,low collaboration efficiency and weak strategy generalization ability existing...This paper proposes a Multi-Agent Attention Proximal Policy Optimization(MA2PPO)algorithm aiming at the problems such as credit assignment,low collaboration efficiency and weak strategy generalization ability existing in the cooperative pursuit tasks of multiple unmanned aerial vehicles(UAVs).Traditional algorithms often fail to effectively identify critical cooperative relationships in such tasks,leading to low capture efficiency and a significant decline in performance when the scale expands.To tackle these issues,based on the proximal policy optimization(PPO)algorithm,MA2PPO adopts the centralized training with decentralized execution(CTDE)framework and introduces a dynamic decoupling mechanism,that is,sharing the multi-head attention(MHA)mechanism for critics during centralized training to solve the credit assignment problem.This method enables the pursuers to identify highly correlated interactions with their teammates,effectively eliminate irrelevant and weakly relevant interactions,and decompose large-scale cooperation problems into decoupled sub-problems,thereby enhancing the collaborative efficiency and policy stability among multiple agents.Furthermore,a reward function has been devised to facilitate the pursuers to encircle the escapee by combining a formation reward with a distance reward,which incentivizes UAVs to develop sophisticated cooperative pursuit strategies.Experimental results demonstrate the effectiveness of the proposed algorithm in achieving multi-UAV cooperative pursuit and inducing diverse cooperative pursuit behaviors among UAVs.Moreover,experiments on scalability have demonstrated that the algorithm is suitable for large-scale multi-UAV systems.展开更多
Continuous control protocols are extensively utilized in traditional MASs,in which information needs to be transmitted among agents consecutively,therefore resulting in excessive consumption of limited resources.To de...Continuous control protocols are extensively utilized in traditional MASs,in which information needs to be transmitted among agents consecutively,therefore resulting in excessive consumption of limited resources.To decrease the control cost,based on ISC,several LFC problems are investigated for second-order MASs without and with time delay,respectively.Firstly,an intermittent sampled controller is designed,and a sufficient and necessary condition is derived,under which state errors between the leader and all the followers approach zero asymptotically.Considering that time delay is inevitable,a new protocol is proposed to deal with the time-delay situation.The error system’s stability is analyzed using the Schur stability theorem,and sufficient and necessary conditions for LFC are obtained,which are closely associated with the coupling gain,the system parameters,and the network structure.Furthermore,for the case where the current position and velocity information are not available,a distributed protocol is designed that depends only on the sampled position information.The sufficient and necessary conditions for LFC are also given.The results show that second-order MASs can achieve the LFC if and only if the system parameters satisfy the inequalities proposed in the paper.Finally,the correctness of the obtained results is verified by numerical simulations.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
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
This paper proposes an output-feedback based prescribed performance consensus tracking control methodology for a class of heterogeneous multi-agent systems(HMASs)with inconsistent system structure,where the performanc...This paper proposes an output-feedback based prescribed performance consensus tracking control methodology for a class of heterogeneous multi-agent systems(HMASs)with inconsistent system structure,where the performance behavior is allowed to be different from that of each other.Both the heterogeneous system structures and the nonidentical performance requirements make the control problem much more challenging than that of MASs with identical structure and performance requirement.This is mainly due to the coupling effect of the system dynamics and performance restriction of each agent in the cooperative control action.The key to solve this problem is to introduce a dual-phase performance-guaranteed method,in which the consensus tracking error is decomposed into auxiliary tracking error and filter tracking error and then the whole performance control is decomposed into two phases.By confining the two errors respectively,the practical tracking error can be proved to be explicitly confined within an arbitrarily given performance envelope by merely adjusting the design parameters rather than modifying control structure.Moreover,the prescribed performance control(PPC)result is not only uniform with any initial conditions and design parameters,allowing it to be global,but also unifying both the global and semi-global result into one frame,distinguishing itself from most existing PPC works where either only global or only semi-global result is guaranteed.Finally,the effectiveness of the proposed control scheme is confirmed by the simulation conducted on a group of tunnel-diode circuits(TDC).展开更多
基金Supported by National Natural Science Foundation of China(60874063)Innovation and Scientific Research Foundation of Graduate Student of Heilongjiang Province(YJSCX2012-263HLJ)
文摘This paper deals with the problem of designing robust sequential covariance intersection(SCI)fusion Kalman filter for the clustering multi-agent sensor network system with measurement delays and uncertain noise variances.The sensor network is partitioned into clusters by the nearest neighbor rule.Using the minimax robust estimation principle,based on the worst-case conservative sensor network system with conservative upper bounds of noise variances,and applying the unbiased linear minimum variance(ULMV)optimal estimation rule,we present the two-layer SCI fusion robust steady-state Kalman filter which can reduce communication and computation burdens and save energy sources,and guarantee that the actual filtering error variances have a less-conservative upper-bound.A Lyapunov equation method for robustness analysis is proposed,by which the robustness of the local and fused Kalman filters is proved.The concept of the robust accuracy is presented and the robust accuracy relations of the local and fused robust Kalman filters are proved.It is proved that the robust accuracy of the global SCI fuser is higher than those of the local SCI fusers and the robust accuracies of all SCI fusers are higher than that of each local robust Kalman filter.A simulation example for a tracking system verifies the robustness and robust accuracy relations.
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supported by the Natural Science Foundation of Jiangsu Province,China(No.BK20240200)in part by the National Natural Science Foundation of China(Nos.62271501,62071488,62471489 and U22B2002)+1 种基金in part by the Key Technologies R&D Program of Jiangsu,China(Prospective and Key Technologies for Industry)(Nos.BE2023022 and BE2023022-4)in part by the Post-doctoral Fellowship Program of CPSF,China(No.GZB20240996).
文摘Due to the characteristics of line-of-sight(LoS)communication in unmanned aerial vehicle(UAV)networks,these systems are highly susceptible to eavesdropping and surveillance.To effectively address the security concerns in UAV communication,covert communication methods have been adopted.This paper explores the joint optimization problem of trajectory and transmission power in a multi-hop UAV relay covert communication system.Considering the communication covertness,power constraints,and trajectory limitations,an algorithm based on multi-agent proximal policy optimization(MAPPO),named covert-MAPPO(C-MAPPO),is proposed.The proposed method leverages the strengths of both optimization algorithms and reinforcement learning to analyze and make joint decisions on the transmission power and flight trajectory strategies for UAVs to achieve cooperation.Simulation results demonstrate that the proposed method can maximize the system throughput while satisfying covertness constraints,and it outperforms benchmark algorithms in terms of system throughput and reward convergence speed.
基金supported in part by National Natural Science Foundation of China(62495095,62088101).
文摘Multi-agent systems(MASs)have demonstrated significant achievements in a wide range of tasks,leveraging their capacity for coordination and adaptation within complex environments.Moreover,the enhancement of their intelligent functionalities is crucial for tackling increasingly challenging tasks.This goal resonates with a paradigm shift within the artificial intelligence(AI)community,from“internet AI”to“embodied AI”,and the MASs with embodied AI are referred to as embodied multi-agent systems(EMASs).An EMAS has the potential to acquire generalized competencies through interactions with environments,enabling it to effectively address a variety of tasks and thereby make a substantial contribution to the quest for artificial general intelligence.Despite the burgeoning interest in this domain,a comprehensive review of EMAS has been lacking.This paper offers analysis and synthesis for EMASs from a control perspective,conceptualizing each embodied agent as an entity equipped with a“brain”for decision and a“body”for environmental interaction.System designs are classified into open-loop,closed-loop,and double-loop categories,and EMAS implementations are discussed.Additionally,the current applications and challenges faced by EMASs are summarized and potential avenues for future research in this field are provided.
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 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National Natural Science Foundation of China(No.12071370)。
文摘This paper focuses on the problem of leaderfollowing consensus for nonlinear cascaded multi-agent systems.The control strategies for these systems are transformed into successive control problem schemes for lower-order error subsystems.A distributed consensus analysis for the corresponding error systems is conducted by employing recursive methods and virtual controllers,accompanied by a series of Lyapunov functions devised throughout the iterative process,which solves the leaderfollowing consensus problem of a class of nonlinear cascaded multi-agent systems.Specific simulation examples illustrate the effectiveness of the proposed control algorithm.
基金supported in part by the National Key Research and Development Program of China under Grant 2022YFB3303900in part by the National Natural Science Foundation of China under Grants 62103277 and 62025305。
文摘In this paper,the distributed optimal formation control problem of heterogeneous Euler–Lagrange multi-agent systems with generic formation constraints and inequality constraints is investigated.Based on the primal–dual dynamics and the adaptive control technique,a distributed optimal formation controller consists of a velocity reference signal generator and a velocity tracking controller is proposed.By using the optimality condition,the relationship between the equilibrium point of the closed-loop system and the optimal solution of the optimization problem is established.Then,by utilizing Lyapunov stability analysis,it is rigorously proved that the optimal formation is reached with the proposed controller.Lastly,simulation examples are provided to substantiate the theoretical results.
文摘This article investigates the time-varying output group formation tracking control(GFTC)problem for heterogeneous multi-agent systems(HMASs)under switching topologies.The objective is to design a distributed control strategy that enables the outputs of the followers to form the desired sub-formations and track the outputs of the leader in each subgroup.Firstly,novel distributed observers are developed to estimate the states of the leaders under switching topologies.Then,GFTC protocols are designed based on the proposed observers.It is shown that with the distributed protocol,the GFTC problem for HMASs under switching topologies is solved if the average dwell time associated with the switching topologies is larger than a fixed threshold.Finally,an example is provided to illustrate the effectiveness of the proposed control strategy.
文摘When a fire breaks out in a high-rise building,the occlusion of smoke and obstacles results in dearth of crucial information concerning people in distress,thereby creating a challenge in their detection.Given the restricted sensing range of a single unmanned aerial vehicle(UAV)cam-era,enhancing the target recognition rate becomes challenging without target information.To tackle this issue,this paper proposes a multi-agent autonomous collaborative detection method for multi-targets in complex fire environments.The objective is to achieve the fusion of multi-angle visual information,effectively increasing the target’s information dimension,and ultimately address-ing the problem of low target recognition rate caused by the lack of target information.The method steps are as follows:first,the you only look once version5(YOLOv5)is used to detect the target in the image;second,the detected targets are tracked to monitor their movements and trajectories;third,the person re-identification(ReID)model is employed to extract the appearance features of targets;finally,by fusing the visual information from multi-angle cameras,the method achieves multi-agent autonomous collaborative detection.The experimental results show that the method effectively combines the visual information from multi-angle cameras,resulting in improved detec-tion efficiency for people in distress.
基金supported by the National Research and Development Program of China under Grant JCKY2018607C019in part by the Key Laboratory Fund of UAV of Northwestern Polytechnical University under Grant 2021JCJQLB0710L.
文摘This paper proposes a Multi-Agent Attention Proximal Policy Optimization(MA2PPO)algorithm aiming at the problems such as credit assignment,low collaboration efficiency and weak strategy generalization ability existing in the cooperative pursuit tasks of multiple unmanned aerial vehicles(UAVs).Traditional algorithms often fail to effectively identify critical cooperative relationships in such tasks,leading to low capture efficiency and a significant decline in performance when the scale expands.To tackle these issues,based on the proximal policy optimization(PPO)algorithm,MA2PPO adopts the centralized training with decentralized execution(CTDE)framework and introduces a dynamic decoupling mechanism,that is,sharing the multi-head attention(MHA)mechanism for critics during centralized training to solve the credit assignment problem.This method enables the pursuers to identify highly correlated interactions with their teammates,effectively eliminate irrelevant and weakly relevant interactions,and decompose large-scale cooperation problems into decoupled sub-problems,thereby enhancing the collaborative efficiency and policy stability among multiple agents.Furthermore,a reward function has been devised to facilitate the pursuers to encircle the escapee by combining a formation reward with a distance reward,which incentivizes UAVs to develop sophisticated cooperative pursuit strategies.Experimental results demonstrate the effectiveness of the proposed algorithm in achieving multi-UAV cooperative pursuit and inducing diverse cooperative pursuit behaviors among UAVs.Moreover,experiments on scalability have demonstrated that the algorithm is suitable for large-scale multi-UAV systems.
基金supported by the National Natural Science Foundation of China under Grants 62476138 and 42375016.
文摘Continuous control protocols are extensively utilized in traditional MASs,in which information needs to be transmitted among agents consecutively,therefore resulting in excessive consumption of limited resources.To decrease the control cost,based on ISC,several LFC problems are investigated for second-order MASs without and with time delay,respectively.Firstly,an intermittent sampled controller is designed,and a sufficient and necessary condition is derived,under which state errors between the leader and all the followers approach zero asymptotically.Considering that time delay is inevitable,a new protocol is proposed to deal with the time-delay situation.The error system’s stability is analyzed using the Schur stability theorem,and sufficient and necessary conditions for LFC are obtained,which are closely associated with the coupling gain,the system parameters,and the network structure.Furthermore,for the case where the current position and velocity information are not available,a distributed protocol is designed that depends only on the sampled position information.The sufficient and necessary conditions for LFC are also given.The results show that second-order MASs can achieve the LFC if and only if the system parameters satisfy the inequalities proposed in the paper.Finally,the correctness of the obtained results is verified by numerical simulations.
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.
基金supported in part by the National Key Research and Development Program of China(2023YFA1011803)the National Natural Science Foundation of China(62273064,W2411061)+1 种基金the Chongqing Natural Science Foundation(CSTB2023NSCQ-MSX0588),the Innovation Support Program for International Students Returning to China(cx2022016)the Central University Project(2023CDJKYJH047).
文摘This paper proposes an output-feedback based prescribed performance consensus tracking control methodology for a class of heterogeneous multi-agent systems(HMASs)with inconsistent system structure,where the performance behavior is allowed to be different from that of each other.Both the heterogeneous system structures and the nonidentical performance requirements make the control problem much more challenging than that of MASs with identical structure and performance requirement.This is mainly due to the coupling effect of the system dynamics and performance restriction of each agent in the cooperative control action.The key to solve this problem is to introduce a dual-phase performance-guaranteed method,in which the consensus tracking error is decomposed into auxiliary tracking error and filter tracking error and then the whole performance control is decomposed into two phases.By confining the two errors respectively,the practical tracking error can be proved to be explicitly confined within an arbitrarily given performance envelope by merely adjusting the design parameters rather than modifying control structure.Moreover,the prescribed performance control(PPC)result is not only uniform with any initial conditions and design parameters,allowing it to be global,but also unifying both the global and semi-global result into one frame,distinguishing itself from most existing PPC works where either only global or only semi-global result is guaranteed.Finally,the effectiveness of the proposed control scheme is confirmed by the simulation conducted on a group of tunnel-diode circuits(TDC).
