时延多智能体系统领导跟随一致性研究
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摘要
为符合实际情形,针对不确定与随机发生非线性的多智能体系统,研究了有时延且网络拓扑切换时系统的领导跟随一致性。传统协议通常保守地假设邻接个体间通信时延与个体和领导者间通信时延大小相同,新协议中上述时延可以大小不同。相比于传统方法,新颖地将复杂网络同步问题研究领域对时延的处理方法引入到多智能体系统一致性问题的研究中,利用一类推广的Halanay不等式,给出系统实现领导跟随一致性需满足的两个与时延无关的充分条件,即时延在相关参数满足定理条件的前提下不影响系统最终实现一致性。相比其他方法得到的含有时延的判定条件,本研究结果保守性更低,实例仿真验证了新协议的可行性。
To make research results more realistic,this paper studied the consensus of multi-agent systems with uncertainties and randomly occurring nonlinearities and time delay via impulsive control with topology switching. In the traditional protocol,it was usually assumed that the communication delay between adjacent individuals was the same as the communication delay between individual and leader,but this was conservative. In the new protocol,the value of delay above could be different.Compared with traditional research methods,the approach that deals with the delay in complex network synchronization research is introduced into the research of consensus of multi-agent systems. Using a generalized Halanay inequality,two sufficient conditions which were not related to the delay were given to meet the leader-following consensus of systems with topology switching,in other words,the delay did not affect the final consensus of system when the relevant parameters satisfied the theorem's conditions. Compared with the decision conditions with delay on other methods,the results of this study are less conservative. The numerical simulation verifies the feasibility of the new protocol.
To make research results more realistic,this paper studied the consensus of multi-agent systems with uncertainties and randomly occurring nonlinearities and time delay via impulsive control with topology switching. In the traditional protocol,it was usually assumed that the communication delay between adjacent individuals was the same as the communication delay between individual and leader,but this was conservative. In the new protocol,the value of delay above could be different.Compared with traditional research methods,the approach that deals with the delay in complex network synchronization research is introduced into the research of consensus of multi-agent systems. Using a generalized Halanay inequality,two sufficient conditions which were not related to the delay were given to meet the leader-following consensus of systems with topology switching,in other words,the delay did not affect the final consensus of system when the relevant parameters satisfied the theorem's conditions. Compared with the decision conditions with delay on other methods,the results of this study are less conservative. The numerical simulation verifies the feasibility of the new protocol.
引文
[1]Han Tao,Guan Zhihong,Chi Ming.Multi-formation control of nonlinear leader-following multi-agent systems[J].ISA Transactions,2017,69(7):140-147.
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[3]Han Tao,Guan Zhihong,Liao Ruiquan.Distributed finite-time formation tracking control of multi-agent systems via FTSMC approach[J].IET Control Theory and Applications,2017,11(15):2585-2590.
[4]Zhou Zhao,Bart D S,Lin Shu.Multi-agent model-based predictive control for large-scale urban traffic networks using a serial scheme[J].IET Control Theory and Applications,2015,9(3):475-484.
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[9]Luo Jie,Cao Chengyu.Consensus in multi-agent systems with nonlinear uncertainties under affixed undirected graph[J].International Journal of Control,Automation and Systems,2014,12(2):231-240.
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[12]Li Hongjie,Zhu Yinglian,Wang Jietai,et al.Consensus of nonlinear second-order multi-agent systems with mixed time-delays and intermittent communications[J].Neurocomputing,2017,251(8):115-126.
[13]Duan Jianmin,Hu Manfeng,Yang Yongqing,et al.A delay-partitioning projection approach to stability analysis of stochastic Markovian jump neural networks with randomly occurred nonlinearities[J].Neurocomputing,2014,128(3):459-465.
[14]Yang Xinsong,Yang Zhichun.Synchronization of TS fuzzy complex dynamical networks with time-varying impulsive delays and stochastic effects[J].Fuzzy Sets and Systems,2014,235(1):25-43.
[15]Qian Yufeng,Wu Xiaoqun,Lyu Jinhu,et al.Second-order consensus of multi-agent systems with nonlinear dynamics via impulsive control[J].Neurocomputing,2014,125(2):142-147.
[16]Li Dandan,Ma Jing,Zhu Hengmin,et al.The consensus of multiagent systems with uncertainties and randomly occurring nonlinearities via impulsive control[J].International Journal of Control Automation and Systems,2016,14(4):1005-1011.
[17]Horn P A,Johnson C R.Matrix analysis[M].Cambridge:Cambridge University Press,1985.
[18]Fang Tao,Sun Jitao.Stability analysis of complex-valued nonlinear differential system[J].Systems&Control Letters,2013,62(10):910-914.
[2]罗贺富,彭世国.多时变时滞的多智能体系统的分布式编队控制[J].广东工业大学学报,2017,34(4):89-96.(Luo Hefu,Peng Shiguo.Distributed formation control of multi-agent systems with coupling time-varying delays[J].Journal of Guangdong University of Technology,2017,34(4):89-96.)
[3]Han Tao,Guan Zhihong,Liao Ruiquan.Distributed finite-time formation tracking control of multi-agent systems via FTSMC approach[J].IET Control Theory and Applications,2017,11(15):2585-2590.
[4]Zhou Zhao,Bart D S,Lin Shu.Multi-agent model-based predictive control for large-scale urban traffic networks using a serial scheme[J].IET Control Theory and Applications,2015,9(3):475-484.
[5]Amini M,Wakolbinger T,Racer M,et al.Alternative supply chain production-sales policies for new product diffusion:an agent-based modeling and simulation approach[J].European Journal of Operational Research,2012,216(2):301-311.
[6]Ibrahim R W,Gani A.A new algorithm in cloud computing of multiagent fractional differential economical system[J].Computing,2016,98(11):1061-1074.
[7]Wautelet Y,Kolp M.Business and model-driven development of BDImulti-agent systems[J].Neurocomputing,2016,182(3):304-321.
[8]Ren Hongwei,Deng Feiqi,Peng Yunjian.Exponential consensus of non-linear stochastic multi-agent systems with ROUs and RONs via impulsive pinning control[J].IET Control Theory and Applications,2017,11(2):225-236.
[9]Luo Jie,Cao Chengyu.Consensus in multi-agent systems with nonlinear uncertainties under affixed undirected graph[J].International Journal of Control,Automation and Systems,2014,12(2):231-240.
[10]Yu Juan,Hu Cheng,Jiang Haijun,et al.Stabilization of nonlinear systems with time-varying delays via impulsive control[J].Neurocomputing,2014,125(3):68-71.
[11]Zhu Wei,Yan Chao.Consensus analysis of second-order agents with active leader and time delay via impulsive control[C]//Proc of the30th Chinese Control Conference.Piscataway,NJ:IEEE Press,2011:4753-4757.
[12]Li Hongjie,Zhu Yinglian,Wang Jietai,et al.Consensus of nonlinear second-order multi-agent systems with mixed time-delays and intermittent communications[J].Neurocomputing,2017,251(8):115-126.
[13]Duan Jianmin,Hu Manfeng,Yang Yongqing,et al.A delay-partitioning projection approach to stability analysis of stochastic Markovian jump neural networks with randomly occurred nonlinearities[J].Neurocomputing,2014,128(3):459-465.
[14]Yang Xinsong,Yang Zhichun.Synchronization of TS fuzzy complex dynamical networks with time-varying impulsive delays and stochastic effects[J].Fuzzy Sets and Systems,2014,235(1):25-43.
[15]Qian Yufeng,Wu Xiaoqun,Lyu Jinhu,et al.Second-order consensus of multi-agent systems with nonlinear dynamics via impulsive control[J].Neurocomputing,2014,125(2):142-147.
[16]Li Dandan,Ma Jing,Zhu Hengmin,et al.The consensus of multiagent systems with uncertainties and randomly occurring nonlinearities via impulsive control[J].International Journal of Control Automation and Systems,2016,14(4):1005-1011.
[17]Horn P A,Johnson C R.Matrix analysis[M].Cambridge:Cambridge University Press,1985.
[18]Fang Tao,Sun Jitao.Stability analysis of complex-valued nonlinear differential system[J].Systems&Control Letters,2013,62(10):910-914.