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 article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system.A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight...This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system.A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight is proposed to improve the traffic efficiency.Firstly a regional multi-agent Q-learning framework is proposed,which can equivalently decompose the global Q value of the traffic system into the local values of several regions Based on the framework and the idea of human-machine cooperation,a dynamic zoning method is designed to divide the traffic network into several strong-coupled regions according to realtime traffic flow densities.In order to achieve better cooperation inside each region,a lightweight spatio-temporal fusion feature extraction network is designed.The experiments in synthetic real-world and city-level scenarios show that the proposed RegionS TLight converges more quickly,is more stable,and obtains better asymptotic performance compared to state-of-theart models.展开更多
To address fixed-time consensus problems of a class of leader-follower second-order nonlinear multi-agent systems with uncertain external disturbances,the event-triggered fixed-time consensus protocol is proposed.Firs...To address fixed-time consensus problems of a class of leader-follower second-order nonlinear multi-agent systems with uncertain external disturbances,the event-triggered fixed-time consensus protocol is proposed.First,the virtual velocity is designed based on the backstepping control method to achieve the system consensus and the bound on convergence time only depending on the system parameters.Second,an event-triggered mechanism is presented to solve the problem of frequent communication between agents,and triggered condition based on state information is given for each follower.It is available to save communication resources,and the Zeno behaviors are excluded.Then,the delay and switching topologies of the system are also discussed.Next,the system stabilization is analyzed by Lyapunov stability theory.Finally,simulation results demonstrate the validity of the presented method.展开更多
As an important mechanism in multi-agent interaction,communication can make agents form complex team relationships rather than constitute a simple set of multiple independent agents.However,the existing communication ...As an important mechanism in multi-agent interaction,communication can make agents form complex team relationships rather than constitute a simple set of multiple independent agents.However,the existing communication schemes can bring much timing redundancy and irrelevant messages,which seriously affects their practical application.To solve this problem,this paper proposes a targeted multiagent communication algorithm based on state control(SCTC).The SCTC uses a gating mechanism based on state control to reduce the timing redundancy of communication between agents and determines the interaction relationship between agents and the importance weight of a communication message through a series connection of hard-and self-attention mechanisms,realizing targeted communication message processing.In addition,by minimizing the difference between the fusion message generated from a real communication message of each agent and a fusion message generated from the buffered message,the correctness of the final action choice of the agent is ensured.Our evaluation using a challenging set of Star Craft II benchmarks indicates that the SCTC can significantly improve the learning performance and reduce the communication overhead between agents,thus ensuring better cooperation between agents.展开更多
The recent proliferation of Fifth-Generation(5G)networks and Sixth-Generation(6G)networks has given rise to Vehicular Crowd Sensing(VCS)systems which solve parking collisions by effectively incentivizing vehicle parti...The recent proliferation of Fifth-Generation(5G)networks and Sixth-Generation(6G)networks has given rise to Vehicular Crowd Sensing(VCS)systems which solve parking collisions by effectively incentivizing vehicle participation.However,instead of being an isolated module,the incentive mechanism usually interacts with other modules.Based on this,we capture this synergy and propose a Collision-free Parking Recommendation(CPR),a novel VCS system framework that integrates an incentive mechanism,a non-cooperative VCS game,and a multi-agent reinforcement learning algorithm,to derive an optimal parking strategy in real time.Specifically,we utilize an LSTM method to predict parking areas roughly for recommendations accurately.Its incentive mechanism is designed to motivate vehicle participation by considering dynamically priced parking tasks and social network effects.In order to cope with stochastic parking collisions,its non-cooperative VCS game further analyzes the uncertain interactions between vehicles in parking decision-making.Then its multi-agent reinforcement learning algorithm models the VCS campaign as a multi-agent Markov decision process that not only derives the optimal collision-free parking strategy for each vehicle independently,but also proves that the optimal parking strategy for each vehicle is Pareto-optimal.Finally,numerical results demonstrate that CPR can accomplish parking tasks at a 99.7%accuracy compared with other baselines,efficiently recommending parking spaces.展开更多
The rapid expansion of enterprises makes product collaborative design （PCD） a critical issue under the distributed heterogeneous environment, but as the collaborative task of large-scale network becomes more complic...The rapid expansion of enterprises makes product collaborative design （PCD） a critical issue under the distributed heterogeneous environment, but as the collaborative task of large-scale network becomes more complicated, neither unified task decomposition and allocation methodology nor Agent-based network management platform can satisfy the increasing demands. In this paper, to meet requirements of PCD for distributed product development, a collaborative design mechanism based on the thought of modularity and the Agent technology is presented. First, the top-down 4-tier process model based on task-oriented modular and Agent is constructed for PCD after analyzing the mapping relationships between requirements and functions in the collaborative design. Second, on basis of sub-task decomposition for PCD based on a mixed method, the mathematic model of task-oriented modular based on multi-objective optimization is established to maximize the module cohesion degree and minimize the module coupling degree, while considering the module executable degree as a restriction. The mathematic model is optimized and simulated by the modified PSO, and the decomposed modules are obtained. Finally, the Agent structure model for collaborative design is put forward, and the optimism matching Agents are selected by using similarity algorithm to implement different task-modules by the integrated reasoning and decision-making mechanism with the behavioral model of collaborative design Agents. With the results of experimental studies for automobile collaborative design, the feasibility and efficiency of this methodology of task-oriented modular and Agent-based collaborative design in the distributed heterogeneous environment are verified. On this basis, an integrative automobile collaborative R＆D platform is developed. This research provides an effective platform for automobile manufacturing enterprises to achieve PCD, and helps to promote product numeralization collaborative R＆D and management development.展开更多
This paper investigates the event-triggered security consensus problem for nonlinear multi-agent systems(MASs)under denial-of-service(Do S)attacks over an undirected graph.A novel adaptive memory observer-based anti-d...This paper investigates the event-triggered security consensus problem for nonlinear multi-agent systems(MASs)under denial-of-service(Do S)attacks over an undirected graph.A novel adaptive memory observer-based anti-disturbance control scheme is presented to improve the observer accuracy by adding a buffer for the system output measurements.Meanwhile,this control scheme can also provide more reasonable control signals when Do S attacks occur.To save network resources,an adaptive memory event-triggered mechanism(AMETM)is also proposed and Zeno behavior is excluded.It is worth mentioning that the AMETM's updates do not require global information.Then,the observer and controller gains are obtained by using the linear matrix inequality(LMI)technique.Finally,simulation examples show the effectiveness of the proposed control scheme.展开更多
This paper investigates the state consensus of linear multi-agent systems in a graph where each agent is equipped with two novel event-triggering mechanisms.Each agent utilizes them to avoid continuous information tra...This paper investigates the state consensus of linear multi-agent systems in a graph where each agent is equipped with two novel event-triggering mechanisms.Each agent utilizes them to avoid continuous information transmissions with its neighbors and to reduce the frequencies of controller updates,respectively.One of the event-triggering mechanisms defines a threshold of state errors by a constant plus a state-dependent variable.The other event-triggering mechanism introduces a period of rest time after each event.For each agent,both event-triggering mechanisms are fully distributed and are independent of any global information.The authors utilize a co-design approach to deal with the interplay between control gains and parameters in event-triggering mechanisms.With appropriate control gains in control laws and parameters in event-triggering conditions,subsystems employing discrete-time signals from neighbors and discrete-time signals from their controllers achieve the state consensus.Simulations are performed to illustrate the effectiveness of the proposed event-triggering mechanisms.展开更多
In this paper,the distributed optimization problem is investigated for a class of general nonlinear model-free multi-agent systems.The dynamical model of each agent is unknown and only the input/output data are availa...In this paper,the distributed optimization problem is investigated for a class of general nonlinear model-free multi-agent systems.The dynamical model of each agent is unknown and only the input/output data are available.A model-free adaptive control method is employed,by which the original unknown nonlinear system is equivalently converted into a dynamic linearized model.An event-triggered consensus scheme is developed to guarantee that the consensus error of the outputs of all agents is convergent.Then,by means of the distributed gradient descent method,a novel event-triggered model-free adaptive distributed optimization algorithm is put forward.Sufficient conditions are established to ensure the consensus and optimality of the addressed system.Finally,simulation results are provided to validate the effectiveness of the proposed approach.展开更多
This paper concerns the event-triggered distributed cross-dimensional formation control problem of heterogeneous multi-agent systems(HMASs)subject to limited network resources.The central aim is to design an effective...This paper concerns the event-triggered distributed cross-dimensional formation control problem of heterogeneous multi-agent systems(HMASs)subject to limited network resources.The central aim is to design an effective distributed formation control scheme that will achieve the desired formation control objectives even in the presence of restricted communication.Consequently,a multi-dimensional HMAS is first developed,where a group of agents are assigned to several subgroups based on their dimensions.Then,to mitigate the excessive consumption of communication resources,a cross-dimensional event-triggered communication mechanism is designed to reduce the information interaction among agents with different dimensions.Under the proposed event-based communication mechanism,the problem of HMAS cross-dimensional formation control is transformed into the asymptotic stability problem of a closed-loop error system.Furthermore,several stability criteria for designing a cross-dimensional formation control protocol and communication schedule are presented in an environment where there is no information interaction among follower agents.Finally,a simulation case study is provided to validate the effectiveness of the proposed formation control protocol.展开更多
Event-triggered consensus in leader-following multi-agent systems with actuator fault is considered in this paper, in which the fault investigated can be multiplicative fault and outage fault. An event-triggered mecha...Event-triggered consensus in leader-following multi-agent systems with actuator fault is considered in this paper, in which the fault investigated can be multiplicative fault and outage fault. An event-triggered mechanism is utilized to relieve the communication burden of the interconnected system. Then, control allocation is proposed to solve actuator fault in the multi-agent systems for the first time. Compared with the existing fault-tolerant methods, the proposed method can guarantee that the consensus errors converge to zero asymptotically without the traditional rank assumption. Meanwhile, the Zeno behavior of the event-triggered system is proved to be avoided. Simulation results are also provided to verify the effectiveness of the proposed method.展开更多
This paper investigates the leader-following consensus problem for multi-agent systems with event-triggered mechanism and control packet losses.Based on the local synchronization error, event-triggered mechanisms are ...This paper investigates the leader-following consensus problem for multi-agent systems with event-triggered mechanism and control packet losses.Based on the local synchronization error, event-triggered mechanisms are proposed in order to reduce the number of controller update.The control packet may lose due to unreliability of communication channel.With the assumption that once the packet loss happens the controller will be set to zero,sufficient consensus criteria for multi-agent system with event-triggered mechanism and control packet losses is obtained.It is also shown that the interplay among the allowable packet loss rate,event-triggered mechanism and sampling period.An illustrative example is given to demonstrate the effectiveness of the theoretical results.展开更多
Allocation of grid resources aims at improving resource utility and grid application performance.Currently,the algorithms proposed for this purpose do not fit well the autonomic,dynamic,distributive and heterogeneous ...Allocation of grid resources aims at improving resource utility and grid application performance.Currently,the algorithms proposed for this purpose do not fit well the autonomic,dynamic,distributive and heterogeneous features of the grid environment.According to MAS(multi-agent system)cooperation mechanism and market bidding game rules,a model of allocating allocation of grid resources based on market economy is introduced to reveal the relationship between supply and demand.This model can make good use of the studying and negotiating ability of consumers'agent and takes full consideration of the consumer's behavior,thus rendering the application and allocation of resource of the consumers rational and valid.In the meantime,the utility function of consumer is given;the existence and the uniqueness of Nash equilibrium point in the resource allocation game and the Nash equilibrium solution are discussed.A dynamic game algorithm of allocating grid resources is designed.Experimental results demonstrate that this algorithm diminishes effectively the unnecessary latency,improves significantly the smoothness of response time,the ratio of throughput and resource utility,thus rendering the supply and demand of the whole grid resource reasonable and the overall grid load balanceable.展开更多
Reinforcement learning has been proven to be an effective approach for solving multi-agent coordination problems in a dynamic open environment.For dealing with multi-agent cooper-ation issues,the mean field multi-agen...Reinforcement learning has been proven to be an effective approach for solving multi-agent coordination problems in a dynamic open environment.For dealing with multi-agent cooper-ation issues,the mean field multi-agent reinforcement leaming method can better overcome the problems of slow learning speed,unstable convergent performance,and poor learning effect.However,the original mean field algorithm cannot extract features well when agents cooperate.In order to solve the large-scale multi-agent coordination problem,in this paper,the mean field multi-agent reinforcement learning algorithm is improved and optimized by combining the multi-head attention mechanism,and the attention-based mean field(MFA)structure is designed.The employment of a multi-head attention mechanism can optimize the interaction among agents,extract more effective cluster features and enable agents to learn more efficient strategies.This paper first introduces the framework structure of MFA and then expounds on the relevant theoretical basis based on the Q-Learning and Actor-Critic algorithms,and finally conducts large-scale multi-agent cooperative experiments on the MAgent platform.The ex-perimental results show that compared with the baseline algorithm,the attention-based mean field Q-learning(MFQA)and attention-based Actor-Critic(MFACA)algorithms can make large-scale multi-agent clusters converge to higher rewards,and perform better than the original mean field multi-agent algorithm.展开更多
Unmanned Underwater Vehicle(UUV)formation system has an important role in the utilization of marine resource.In order to provide an efficient method to research modeling and simulation of UUV formation in the marine ...Unmanned Underwater Vehicle(UUV)formation system has an important role in the utilization of marine resource.In order to provide an efficient method to research modeling and simulation of UUV formation in the marine environment,the novel approach based on Multi-Agent Interaction Chain was proposed for the UUV formation system.Firstly,Multi-Agent Interaction Chain was analyzed,which mainly considered task and role of UUV in the formation,and the overall modeling process of UUV formation system based on Multi-Agent Interaction Chain was established.Then,the static structure of Multi-Agent Interaction Chain was researched focusing on Hybrid UUV-Agent model structure from the UUV-Agent State-Set and UUV-Agent Rule-Base which were the two aspects to strengthen reliability of interaction chain;the dynamic mechanism of Multi-Agent Interaction Chain was designed,which was focused on collaboration model and communication model through the Adaptive Dynamic Contract Net Protocol and KQML/XML/RTI.Finally,three experiments were established to verify the validity and effectiveness of proposed modeling approach for UUV formation system.Simulation results show the proposed model has good performance,which has important theoretical innovation and application prospects.展开更多
The problem of secure consensus for multi-agent systems(MASs)is tackled in this study.The self-triggering strategy is designed to enable each healthy agent to estimate its next triggering step at the current triggerin...The problem of secure consensus for multi-agent systems(MASs)is tackled in this study.The self-triggering strategy is designed to enable each healthy agent to estimate its next triggering step at the current triggering step.Thus,each healthy agent only needs to sense and broadcast at its triggering steps,and to monitor the latest broadcast states of their neighbors at their triggering steps.The frequent monitoring is thereby mitigated.Subsequently,a self-triggering secure consensus algorithm is developed to guarantee that the state variables of healthy agents reach consensus despite the influence of faulty agents in the network.The convergence analysis of the proposed method is conducted with graph tools and Lyapunov theory.Numerical examples are given to illustrate the superior performance of the proposed self-triggering secure consensus algorithm compared with the existing methods based on the static and dynamic event-triggering mechanisms.展开更多
In this paper,we investigate the observer-based event-triggered consensus problem for linear multi-agent systems(MASs)under a directed graph and denial-of-service(DoS)attacks.A type of DoS attacks launched by maliciou...In this paper,we investigate the observer-based event-triggered consensus problem for linear multi-agent systems(MASs)under a directed graph and denial-of-service(DoS)attacks.A type of DoS attacks launched by malicious attackers at irregular intervals is considered,which can cause communication channel disruption.A novel event-triggered secure control scheme based on a closed-loop observer is proposed to determine the scheduling of the controller update,and a separation method with less conservativeness is employed to design the controller and observer gains.Then,the frequency and duration of DoS attacks that can be tolerated are analysed for the observer-based secure consensus problem.In addition,a strictly positive minimal event-triggered time interval for each agent is designed with the help of the proposed eventtriggered condition to eliminate the Zeno behaviour.Finally,a numerical simulation is given to verify the theoretical analysis.展开更多
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 Science and Technology Major Project(2021ZD0112702)the National Natural Science Foundation(NNSF)of China(62373100,62233003)the Natural Science Foundation of Jiangsu Province of China(BK20202006)。
文摘This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system.A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight is proposed to improve the traffic efficiency.Firstly a regional multi-agent Q-learning framework is proposed,which can equivalently decompose the global Q value of the traffic system into the local values of several regions Based on the framework and the idea of human-machine cooperation,a dynamic zoning method is designed to divide the traffic network into several strong-coupled regions according to realtime traffic flow densities.In order to achieve better cooperation inside each region,a lightweight spatio-temporal fusion feature extraction network is designed.The experiments in synthetic real-world and city-level scenarios show that the proposed RegionS TLight converges more quickly,is more stable,and obtains better asymptotic performance compared to state-of-theart models.
基金National Natural Science Foundation of China(No.62073296)Natural Science Foundation of Zhejiang Province,China(No.LZ23F030010)Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province,China Jiliang University(No.ZNZZSZ-CJLU2022-03)Rights and permissions。
文摘To address fixed-time consensus problems of a class of leader-follower second-order nonlinear multi-agent systems with uncertain external disturbances,the event-triggered fixed-time consensus protocol is proposed.First,the virtual velocity is designed based on the backstepping control method to achieve the system consensus and the bound on convergence time only depending on the system parameters.Second,an event-triggered mechanism is presented to solve the problem of frequent communication between agents,and triggered condition based on state information is given for each follower.It is available to save communication resources,and the Zeno behaviors are excluded.Then,the delay and switching topologies of the system are also discussed.Next,the system stabilization is analyzed by Lyapunov stability theory.Finally,simulation results demonstrate the validity of the presented method.
文摘As an important mechanism in multi-agent interaction,communication can make agents form complex team relationships rather than constitute a simple set of multiple independent agents.However,the existing communication schemes can bring much timing redundancy and irrelevant messages,which seriously affects their practical application.To solve this problem,this paper proposes a targeted multiagent communication algorithm based on state control(SCTC).The SCTC uses a gating mechanism based on state control to reduce the timing redundancy of communication between agents and determines the interaction relationship between agents and the importance weight of a communication message through a series connection of hard-and self-attention mechanisms,realizing targeted communication message processing.In addition,by minimizing the difference between the fusion message generated from a real communication message of each agent and a fusion message generated from the buffered message,the correctness of the final action choice of the agent is ensured.Our evaluation using a challenging set of Star Craft II benchmarks indicates that the SCTC can significantly improve the learning performance and reduce the communication overhead between agents,thus ensuring better cooperation between agents.
基金supported in part by the Natural Science Foundation of Shandong Province of China(ZR202103040180)the Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-004the Fundamental Research Funds for the Central Universities under Grant 20CX05019A.
文摘The recent proliferation of Fifth-Generation(5G)networks and Sixth-Generation(6G)networks has given rise to Vehicular Crowd Sensing(VCS)systems which solve parking collisions by effectively incentivizing vehicle participation.However,instead of being an isolated module,the incentive mechanism usually interacts with other modules.Based on this,we capture this synergy and propose a Collision-free Parking Recommendation(CPR),a novel VCS system framework that integrates an incentive mechanism,a non-cooperative VCS game,and a multi-agent reinforcement learning algorithm,to derive an optimal parking strategy in real time.Specifically,we utilize an LSTM method to predict parking areas roughly for recommendations accurately.Its incentive mechanism is designed to motivate vehicle participation by considering dynamically priced parking tasks and social network effects.In order to cope with stochastic parking collisions,its non-cooperative VCS game further analyzes the uncertain interactions between vehicles in parking decision-making.Then its multi-agent reinforcement learning algorithm models the VCS campaign as a multi-agent Markov decision process that not only derives the optimal collision-free parking strategy for each vehicle independently,but also proves that the optimal parking strategy for each vehicle is Pareto-optimal.Finally,numerical results demonstrate that CPR can accomplish parking tasks at a 99.7%accuracy compared with other baselines,efficiently recommending parking spaces.
基金Supported by National Science and Technology Major Project of China(Grant No.2009ZX04014-103)PhD Programs Foundation of Ministry of Education of China(Grant No.20100072110038)+1 种基金National Natural Science Foundation of China(Grant Nos.61075064,61034004,61005090)Program for New Century Excellent Talents in University of Ministry of Education of China(Grant No.NECT-10-0633)
文摘The rapid expansion of enterprises makes product collaborative design （PCD） a critical issue under the distributed heterogeneous environment, but as the collaborative task of large-scale network becomes more complicated, neither unified task decomposition and allocation methodology nor Agent-based network management platform can satisfy the increasing demands. In this paper, to meet requirements of PCD for distributed product development, a collaborative design mechanism based on the thought of modularity and the Agent technology is presented. First, the top-down 4-tier process model based on task-oriented modular and Agent is constructed for PCD after analyzing the mapping relationships between requirements and functions in the collaborative design. Second, on basis of sub-task decomposition for PCD based on a mixed method, the mathematic model of task-oriented modular based on multi-objective optimization is established to maximize the module cohesion degree and minimize the module coupling degree, while considering the module executable degree as a restriction. The mathematic model is optimized and simulated by the modified PSO, and the decomposed modules are obtained. Finally, the Agent structure model for collaborative design is put forward, and the optimism matching Agents are selected by using similarity algorithm to implement different task-modules by the integrated reasoning and decision-making mechanism with the behavioral model of collaborative design Agents. With the results of experimental studies for automobile collaborative design, the feasibility and efficiency of this methodology of task-oriented modular and Agent-based collaborative design in the distributed heterogeneous environment are verified. On this basis, an integrative automobile collaborative R＆D platform is developed. This research provides an effective platform for automobile manufacturing enterprises to achieve PCD, and helps to promote product numeralization collaborative R＆D and management development.
基金supported by the National Natural Science Foundation of China(61773056)the Scientific and Technological Innovation Foundation of Shunde Graduate School,University of Science and Technology Beijing(USTB)(BK19AE018)+2 种基金the Fundamental Research Funds for the Central Universities of USTB(FRF-TP-20-09B,230201606500061,FRF-DF-20-35,FRF-BD-19-002A)supported by Zhejiang Natural Science Foundation(LD21F030001)supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(Ministry of Science and Information and Communications Technology)(NRF-2020R1A2C1005449)。
文摘This paper investigates the event-triggered security consensus problem for nonlinear multi-agent systems(MASs)under denial-of-service(Do S)attacks over an undirected graph.A novel adaptive memory observer-based anti-disturbance control scheme is presented to improve the observer accuracy by adding a buffer for the system output measurements.Meanwhile,this control scheme can also provide more reasonable control signals when Do S attacks occur.To save network resources,an adaptive memory event-triggered mechanism(AMETM)is also proposed and Zeno behavior is excluded.It is worth mentioning that the AMETM's updates do not require global information.Then,the observer and controller gains are obtained by using the linear matrix inequality(LMI)technique.Finally,simulation examples show the effectiveness of the proposed control scheme.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.61873074 and 61903140in part by the Fundamental Research Funds for the Central Universities under Grant No.2020MS019。
文摘This paper investigates the state consensus of linear multi-agent systems in a graph where each agent is equipped with two novel event-triggering mechanisms.Each agent utilizes them to avoid continuous information transmissions with its neighbors and to reduce the frequencies of controller updates,respectively.One of the event-triggering mechanisms defines a threshold of state errors by a constant plus a state-dependent variable.The other event-triggering mechanism introduces a period of rest time after each event.For each agent,both event-triggering mechanisms are fully distributed and are independent of any global information.The authors utilize a co-design approach to deal with the interplay between control gains and parameters in event-triggering mechanisms.With appropriate control gains in control laws and parameters in event-triggering conditions,subsystems employing discrete-time signals from neighbors and discrete-time signals from their controllers achieve the state consensus.Simulations are performed to illustrate the effectiveness of the proposed event-triggering mechanisms.
基金Project supported by the National Natural Science Foundation of China(No.62003213)。
文摘In this paper,the distributed optimization problem is investigated for a class of general nonlinear model-free multi-agent systems.The dynamical model of each agent is unknown and only the input/output data are available.A model-free adaptive control method is employed,by which the original unknown nonlinear system is equivalently converted into a dynamic linearized model.An event-triggered consensus scheme is developed to guarantee that the consensus error of the outputs of all agents is convergent.Then,by means of the distributed gradient descent method,a novel event-triggered model-free adaptive distributed optimization algorithm is put forward.Sufficient conditions are established to ensure the consensus and optimality of the addressed system.Finally,simulation results are provided to validate the effectiveness of the proposed approach.
基金supported by the National Natural Science Foundation of China(No.62173218)the International Cooperation Project of Shanghai Science and Technology Commission,China(No.21190780300)。
文摘This paper concerns the event-triggered distributed cross-dimensional formation control problem of heterogeneous multi-agent systems(HMASs)subject to limited network resources.The central aim is to design an effective distributed formation control scheme that will achieve the desired formation control objectives even in the presence of restricted communication.Consequently,a multi-dimensional HMAS is first developed,where a group of agents are assigned to several subgroups based on their dimensions.Then,to mitigate the excessive consumption of communication resources,a cross-dimensional event-triggered communication mechanism is designed to reduce the information interaction among agents with different dimensions.Under the proposed event-based communication mechanism,the problem of HMAS cross-dimensional formation control is transformed into the asymptotic stability problem of a closed-loop error system.Furthermore,several stability criteria for designing a cross-dimensional formation control protocol and communication schedule are presented in an environment where there is no information interaction among follower agents.Finally,a simulation case study is provided to validate the effectiveness of the proposed formation control protocol.
基金supported by the National Key Research and Development Program of China (Grant No. 2018YFB1307803)the National Natural Science Foundation of China (Grant No. 61973175)。
文摘Event-triggered consensus in leader-following multi-agent systems with actuator fault is considered in this paper, in which the fault investigated can be multiplicative fault and outage fault. An event-triggered mechanism is utilized to relieve the communication burden of the interconnected system. Then, control allocation is proposed to solve actuator fault in the multi-agent systems for the first time. Compared with the existing fault-tolerant methods, the proposed method can guarantee that the consensus errors converge to zero asymptotically without the traditional rank assumption. Meanwhile, the Zeno behavior of the event-triggered system is proved to be avoided. Simulation results are also provided to verify the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China under Grant Nos.61573156,61733008,and 61503142the Natural Science Foundation of Guangdong Province under Grant No.2017A030313332the Fundamental Research Funds for the Central Universities under Grant Nos.x2zdD 2153620 and x2zdD 2173280
文摘This paper investigates the leader-following consensus problem for multi-agent systems with event-triggered mechanism and control packet losses.Based on the local synchronization error, event-triggered mechanisms are proposed in order to reduce the number of controller update.The control packet may lose due to unreliability of communication channel.With the assumption that once the packet loss happens the controller will be set to zero,sufficient consensus criteria for multi-agent system with event-triggered mechanism and control packet losses is obtained.It is also shown that the interplay among the allowable packet loss rate,event-triggered mechanism and sampling period.An illustrative example is given to demonstrate the effectiveness of the theoretical results.
基金Supported by the Natural Science Foundation of Hunan Province(Grant No.06JJ2033)the Society Science Foundation of Hunan Province(Grant No.07YBB239)
文摘Allocation of grid resources aims at improving resource utility and grid application performance.Currently,the algorithms proposed for this purpose do not fit well the autonomic,dynamic,distributive and heterogeneous features of the grid environment.According to MAS(multi-agent system)cooperation mechanism and market bidding game rules,a model of allocating allocation of grid resources based on market economy is introduced to reveal the relationship between supply and demand.This model can make good use of the studying and negotiating ability of consumers'agent and takes full consideration of the consumer's behavior,thus rendering the application and allocation of resource of the consumers rational and valid.In the meantime,the utility function of consumer is given;the existence and the uniqueness of Nash equilibrium point in the resource allocation game and the Nash equilibrium solution are discussed.A dynamic game algorithm of allocating grid resources is designed.Experimental results demonstrate that this algorithm diminishes effectively the unnecessary latency,improves significantly the smoothness of response time,the ratio of throughput and resource utility,thus rendering the supply and demand of the whole grid resource reasonable and the overall grid load balanceable.
基金supported by the National Key R&D Program of China under Grant No.2022ZD0120002the National Natural Science Foundation of China under Grant Nos.62233004 and 62073076the Jiangsu Provincial Key Laboratory of Networked Collective Intelligence under Grant No.BM2017002。
文摘Reinforcement learning has been proven to be an effective approach for solving multi-agent coordination problems in a dynamic open environment.For dealing with multi-agent cooper-ation issues,the mean field multi-agent reinforcement leaming method can better overcome the problems of slow learning speed,unstable convergent performance,and poor learning effect.However,the original mean field algorithm cannot extract features well when agents cooperate.In order to solve the large-scale multi-agent coordination problem,in this paper,the mean field multi-agent reinforcement learning algorithm is improved and optimized by combining the multi-head attention mechanism,and the attention-based mean field(MFA)structure is designed.The employment of a multi-head attention mechanism can optimize the interaction among agents,extract more effective cluster features and enable agents to learn more efficient strategies.This paper first introduces the framework structure of MFA and then expounds on the relevant theoretical basis based on the Q-Learning and Actor-Critic algorithms,and finally conducts large-scale multi-agent cooperative experiments on the MAgent platform.The ex-perimental results show that compared with the baseline algorithm,the attention-based mean field Q-learning(MFQA)and attention-based Actor-Critic(MFACA)algorithms can make large-scale multi-agent clusters converge to higher rewards,and perform better than the original mean field multi-agent algorithm.
基金This work was supported by the Ship pre-research support technology fund(11J4.1.1).
文摘Unmanned Underwater Vehicle(UUV)formation system has an important role in the utilization of marine resource.In order to provide an efficient method to research modeling and simulation of UUV formation in the marine environment,the novel approach based on Multi-Agent Interaction Chain was proposed for the UUV formation system.Firstly,Multi-Agent Interaction Chain was analyzed,which mainly considered task and role of UUV in the formation,and the overall modeling process of UUV formation system based on Multi-Agent Interaction Chain was established.Then,the static structure of Multi-Agent Interaction Chain was researched focusing on Hybrid UUV-Agent model structure from the UUV-Agent State-Set and UUV-Agent Rule-Base which were the two aspects to strengthen reliability of interaction chain;the dynamic mechanism of Multi-Agent Interaction Chain was designed,which was focused on collaboration model and communication model through the Adaptive Dynamic Contract Net Protocol and KQML/XML/RTI.Finally,three experiments were established to verify the validity and effectiveness of proposed modeling approach for UUV formation system.Simulation results show the proposed model has good performance,which has important theoretical innovation and application prospects.
基金supported by the National Natural Science Foundation of China(Grant Nos.62173147,62303030,62403028 and U2233212)Beijing Municipal Natural Science Foundation(Grant No.L221008)+1 种基金Open Fund of Science and Technology on Thermal Energy and Power Laboratory(Grant No.TPL2022C02)Postdoctoral Fellowship Program of CPSF(Grant No.GZC20233377)
文摘The problem of secure consensus for multi-agent systems(MASs)is tackled in this study.The self-triggering strategy is designed to enable each healthy agent to estimate its next triggering step at the current triggering step.Thus,each healthy agent only needs to sense and broadcast at its triggering steps,and to monitor the latest broadcast states of their neighbors at their triggering steps.The frequent monitoring is thereby mitigated.Subsequently,a self-triggering secure consensus algorithm is developed to guarantee that the state variables of healthy agents reach consensus despite the influence of faulty agents in the network.The convergence analysis of the proposed method is conducted with graph tools and Lyapunov theory.Numerical examples are given to illustrate the superior performance of the proposed self-triggering secure consensus algorithm compared with the existing methods based on the static and dynamic event-triggering mechanisms.
基金the National Natural Science Foundation of China[61873338]Natural Science Foundation of Shandong Province[ZR2020KF034]Taishan Scholars[tsqn201812052].
文摘In this paper,we investigate the observer-based event-triggered consensus problem for linear multi-agent systems(MASs)under a directed graph and denial-of-service(DoS)attacks.A type of DoS attacks launched by malicious attackers at irregular intervals is considered,which can cause communication channel disruption.A novel event-triggered secure control scheme based on a closed-loop observer is proposed to determine the scheduling of the controller update,and a separation method with less conservativeness is employed to design the controller and observer gains.Then,the frequency and duration of DoS attacks that can be tolerated are analysed for the observer-based secure consensus problem.In addition,a strictly positive minimal event-triggered time interval for each agent is designed with the help of the proposed eventtriggered condition to eliminate the Zeno behaviour.Finally,a numerical simulation is given to verify the theoretical analysis.
