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Decentralized adaptive synchronization with bounded identification errors for discrete-time nonlinear multi-agent systems with unknown parameters and unknown high-frequency gains

机译:参数未知,高频增益未知的离散时间非线性多智能体系统的有界误差的分散自适应同步

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摘要

This paper addresses the challenging problem of decentralized adaptive control for a class of coupled hidden leader-follower multi-agent systems, in which each agent is described by a nonlinearly parame-terized uncertain model in discrete time and can interact with its neighbors via the history information from its neighbors. One of the agents is a leader, who knows the desired reference trajectory, while other agents cannot receive the desired reference signal or are unaware of existence of the leader. In order to tackle unknown internal parameters and unknown high-frequency gains, a projection-type parameter estimation algorithm is proposed. Based on the certainty equivalence principle and neighborhood history information, the decentralized adaptive control is designed, under which, the boundedness of identification error is guaranteed with the help of the Lyapunov theory. Under some conditions, it is shown that the multi-agent system eventually achieves synchronization in the presence of strong couplings. Finally, a simulation example is given to support the results of the proposed scheme. (c) 2017 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
机译:本文针对一类耦合的隐式跟随跟随多智能体系统,解决了分散自适应控制的挑战性问题,其中每个智能体均由非线性参数化不确定模型在离散时间内描述,并且可以通过历史与其邻居进行交互来自邻居的信息。特工之一是领导者,他知道所需的参考轨迹,而其他特工不能接收所需的参考信号或不知道该领导者的存在。为了解决未知的内部参数和未知的高频增益,提出了一种投影型参数估计算法。基于确定性等价原理和邻域历史信息,设计了分散自适应控制,在此基础上,利用李雅普诺夫理论保证了辨识误差的有界性。在某些情况下,表明在具有强耦合的情况下,多主体系统最终实现了同步。最后,给出了一个仿真实例来支持所提出方案的结果。 (c)2017富兰克林研究所。由Elsevier Ltd.出版。保留所有权利。

著录项

  • 来源
    《Journal of the Franklin Institute》 |2018年第1期|474-500|共27页
  • 作者

    Zhang Xinghong; Ma Hongbin;

  • 作者单位

    Beijing Inst Technol, Sch Automat, Beijing, Peoples R China;

    Beijing Inst Technol, Sch Automat, Beijing, Peoples R China;

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  • 正文语种 eng
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  • 入库时间 2022-08-18 02:57:35

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