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首页> 外文期刊>International Journal of Control >Cooperative tracking and estimation of linear multi-agent systems with a dynamic leader via iterative learning
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Cooperative tracking and estimation of linear multi-agent systems with a dynamic leader via iterative learning

机译：通过迭代学习与动态领导者协同跟踪和估计线性多智能体系统

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This paper presents a new iterative learning design approach for cooperative tracking and estimation of linear multi-agent systems with a dynamic leader. The input of the dynamic leader is unavailable to all follower agents. Distributed iterative learning controllers, based on the relative state information of neighbouring agents, are proposed for tracking the dynamic leader, and meanwhile identifying the unknown input of the leader. Stability and convergence of the proposed controller are established using Lyapunov-Krasovskii theory. Further, this result is extended to the output feedback case where only partial states can be measured. A local observer is constructed to recover the unmeasurable states. Then, distributed iterative learning tracking controllers, based on the relative observed states of neighbouring agents, are devised. For both cases, the main advantage of the proposed controllers allows for tracking the dynamic leader for undirected graphs without knowing the input of the leader, and meanwhile identifying the unknown input of the leader using distributed iterative learning laws. An example is given to show the efficacy and utility of the theoretical results.
机译：本文提出了一种新的迭代学习设计方法，用于具有动态领导者的线性多主体系统的协同跟踪和估计。动态领导者的输入不适用于所有关注者代理。提出了一种基于相邻智能体的相对状态信息的分布式迭代学习控制器，用于跟踪动态领导者，同时识别领导者的未知输入。利用Lyapunov-Krasovskii理论建立了所提出控制器的稳定性和收敛性。此外，该结果扩展到只能测量部分状态的输出反馈情况。构造了本地观察者以恢复无法测量的状态。然后，基于相邻代理的相对观察状态，设计了分布式迭代学习跟踪控制器。对于这两种情况，所提出的控制器的主要优点都允许在不知道引导者输入的情况下跟踪无向图的动态引导者，同时使用分布式迭代学习定律来识别引导者的未知输入。举例说明了理论结果的有效性和实用性。

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