多智能体系统的自适应事件触发平均一致性
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摘要
针对一阶多智能体系统的平均一致性问题,本文基于自适应事件的控制方法,研究了具有无向连通拓扑结构的一阶多智能体系统的平均一致性。为了确保平均一致性成立,并使系统的控制性能得到提高,分析了多智能体系统通信拓扑图的特性,设计了一种基于事件的自适应控制协议。该协议利用基于状态误差的自适应事件触发条件,使智能体之间的信息传递仅在触发条件成立的时刻进行。同时,基于Lyapunov稳定性理论,得到系统实现平均一致的充分条件,对固定拓扑和切换拓扑情形进行研究,通过Matlab软件进行仿真分析。仿真结果表明,在自适应事件触发控制下,系统的事件触发频率更快,使系统的控制性能得到提高,验证了理论结果的有效性。该研究为多机器人系统的协作问题提供了理论基础。
In this paper,in order to solve the average consensus problem of first-order multi-agent systems,we investigate the adaptive event-based average consensus of first-order multi-agent systems with undirected connected topology.In order to ensure average consensus and improve the control performance of the system,we investigate the properties of the multi-agent system communication topology.Meanwhile,we establish an adaptive control protocol based on events.The protocol takes advantage of adaptive event-triggering conditions based on state errors,so that information transmission between agents can only be carried out at the time when triggering conditions are satisfied.Simultaneously,by using Lyapunov stability theory,sufficient conditions guaranteeing the system to asymptotically achieve average consensus are given.Both fixed topology and switching topologies are investigated,and the Matlab software is used for simulation analysis.Simulation results show that the system triggers faster frequency under adaptive event trigger control,the control performance of the system is improved,and the validity of the theoretical results is verified.This research provides a theoretical basis for the collaboration of multi-robot systems.
In this paper,in order to solve the average consensus problem of first-order multi-agent systems,we investigate the adaptive event-based average consensus of first-order multi-agent systems with undirected connected topology.In order to ensure average consensus and improve the control performance of the system,we investigate the properties of the multi-agent system communication topology.Meanwhile,we establish an adaptive control protocol based on events.The protocol takes advantage of adaptive event-triggering conditions based on state errors,so that information transmission between agents can only be carried out at the time when triggering conditions are satisfied.Simultaneously,by using Lyapunov stability theory,sufficient conditions guaranteeing the system to asymptotically achieve average consensus are given.Both fixed topology and switching topologies are investigated,and the Matlab software is used for simulation analysis.Simulation results show that the system triggers faster frequency under adaptive event trigger control,the control performance of the system is improved,and the validity of the theoretical results is verified.This research provides a theoretical basis for the collaboration of multi-robot systems.
引文
[1]Zhang H T,Chen M Z Q,Stan G B.Fast consensus viapredictive pinning control[J].IEEE Transactions on Circuitsand Systems I:Regular Papers,2011,58(9):2247-2258.
[2]Wen G,Duan Z,Su H,et al.A connectivity-preservingflocking algorithm for multi-agent dynamical systemswith bounded potential function[J].IET Control Theoryand Applications,2012,6(6):813-821.
[3]李淑琴,唐振民,赵春霞,等.一种基于多智能体系统的机器人群组织形式[J].计算机应用研究,2004,21(1):61-63.
[4]SavKin A V.Coordinate collective motion of groups of autonomous mobile robots:analysis of visek’s model[J].IEEE Transactions on Automatic Control.2004,49(6):981-982.
[5]余宏旺,郑毓蕃.多智能体系统在分布式采样控制下的动力学行为[J].自动化学报,2012,38(3):357-365.
[6]Cao M T,Xiao F,Wang L.Event-based second-order consensus control for multi-agent systems via synchronous periodic event detection[J].IEEE Transaction on Automatic Control,2015,60(9):2452-2457.
[7]Wu Y J,Wang L.Sampled-data consensus for multi-agent systems with quantized communication[J].International Journal of Control.2015,88(2):413-428.
[8]Meng X Y,Chen T W.Optimal sampling and performance comparison of periodic and event Based impulse control[J].IEEETransaction on Automatic Control,2012,57(12):3252-3259.
[9]Dimarogonas D V,Johansson K H.Event-triggered control for multi-agent systems[C]∥Proceedings of the 28th Chinese Control Conference on Decision and Control.Shanghai,China:IEEE,2009:7131-7136.
[10]Yan H C,Shen Y C,Zhang H,et al.Decentralized event-triggered consensus control for second-order multi-agent systems[J].Neurocomputing,2014,133(1):18-24.
[11]夏倩倩,刘开恩,纪志坚.基于二阶邻居事件触发多智能体系统的一致性[J].智能系统学报,2017,12(6):833-840.
[12]Zhu W,Jiang Z P,Feng G.Event-based consensus of multi-agent systems with general linear models[J].Automatica,2014,50(2):552-558.
[13]Li H Q,Chen G,Huang T W,et al.Event-triggered consensus in nonlinear multi-agent systemswith nonlinear dynamics and directed network topology[J].Journal of the Franklin Institute,2014,351(5):2582-2599.
[14]Liu K E,Ji Z J.Periodic event-triggered consensus of time-delayed multi-agent systems under switching topologies[C]∥Proceedings of the 36th Chinese Control Conference.Dalian,China:Chinese Control Conference,2017:8230-8235.
[15]Liu K E,Ji Z J.Consensus of multi-agent systems with time delay based on periodic sample and event hybrid control[J].Neurocomputing,2017,270(27):11-17.
[16]刘丹,胡爱花,邵浩宇.自适应事件触发控制的多智能体系统一致性[J].计算机工程与应用,2017,53(1):44-48.
[17]Zhao R,Zhu M L,Xu Y.Adaptive tracking control for multi-agent systems[J].Computer Engineeringand Applications,2017,53(18):39-43.
[18]Ren W,Beard R W.Consensus seeking in multi-agentsystems under dynamically changing interaction topologies[J].IEEETransactions on Automatic Control,2005,50(5):655-661.
[19]陈复扬,姜斌.自适应控制与应用[M].北京:国防工业出版社,2009.
[20]Horn R A,Johnson C R.Matrix analysis[M].Cambridge:Cambridge University Press,2012.
[2]Wen G,Duan Z,Su H,et al.A connectivity-preservingflocking algorithm for multi-agent dynamical systemswith bounded potential function[J].IET Control Theoryand Applications,2012,6(6):813-821.
[3]李淑琴,唐振民,赵春霞,等.一种基于多智能体系统的机器人群组织形式[J].计算机应用研究,2004,21(1):61-63.
[4]SavKin A V.Coordinate collective motion of groups of autonomous mobile robots:analysis of visek’s model[J].IEEE Transactions on Automatic Control.2004,49(6):981-982.
[5]余宏旺,郑毓蕃.多智能体系统在分布式采样控制下的动力学行为[J].自动化学报,2012,38(3):357-365.
[6]Cao M T,Xiao F,Wang L.Event-based second-order consensus control for multi-agent systems via synchronous periodic event detection[J].IEEE Transaction on Automatic Control,2015,60(9):2452-2457.
[7]Wu Y J,Wang L.Sampled-data consensus for multi-agent systems with quantized communication[J].International Journal of Control.2015,88(2):413-428.
[8]Meng X Y,Chen T W.Optimal sampling and performance comparison of periodic and event Based impulse control[J].IEEETransaction on Automatic Control,2012,57(12):3252-3259.
[9]Dimarogonas D V,Johansson K H.Event-triggered control for multi-agent systems[C]∥Proceedings of the 28th Chinese Control Conference on Decision and Control.Shanghai,China:IEEE,2009:7131-7136.
[10]Yan H C,Shen Y C,Zhang H,et al.Decentralized event-triggered consensus control for second-order multi-agent systems[J].Neurocomputing,2014,133(1):18-24.
[11]夏倩倩,刘开恩,纪志坚.基于二阶邻居事件触发多智能体系统的一致性[J].智能系统学报,2017,12(6):833-840.
[12]Zhu W,Jiang Z P,Feng G.Event-based consensus of multi-agent systems with general linear models[J].Automatica,2014,50(2):552-558.
[13]Li H Q,Chen G,Huang T W,et al.Event-triggered consensus in nonlinear multi-agent systemswith nonlinear dynamics and directed network topology[J].Journal of the Franklin Institute,2014,351(5):2582-2599.
[14]Liu K E,Ji Z J.Periodic event-triggered consensus of time-delayed multi-agent systems under switching topologies[C]∥Proceedings of the 36th Chinese Control Conference.Dalian,China:Chinese Control Conference,2017:8230-8235.
[15]Liu K E,Ji Z J.Consensus of multi-agent systems with time delay based on periodic sample and event hybrid control[J].Neurocomputing,2017,270(27):11-17.
[16]刘丹,胡爱花,邵浩宇.自适应事件触发控制的多智能体系统一致性[J].计算机工程与应用,2017,53(1):44-48.
[17]Zhao R,Zhu M L,Xu Y.Adaptive tracking control for multi-agent systems[J].Computer Engineeringand Applications,2017,53(18):39-43.
[18]Ren W,Beard R W.Consensus seeking in multi-agentsystems under dynamically changing interaction topologies[J].IEEETransactions on Automatic Control,2005,50(5):655-661.
[19]陈复扬,姜斌.自适应控制与应用[M].北京:国防工业出版社,2009.
[20]Horn R A,Johnson C R.Matrix analysis[M].Cambridge:Cambridge University Press,2012.