Distributed Fault-Tolerant Control of Multiagent Systems: An Adaptive Learning Approach
Distributed Fault-Tolerant Control of Multiagent Systems: An Adaptive Learning Approach
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DOI：
10.1109/tnnls.2019.2904277
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发表时间：
2020-02
影响因子：
10.4
通讯作者：
M. Khalili;Xiaodong Zhang;Yongcan Cao;M. Polycarpou;T. Parisini
中科院分区：
文献类型：
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作者：
M. Khalili;Xiaodong Zhang;Yongcan Cao;M. Polycarpou;T. Parisini
针对一类高阶非线性不确定多智能体系统，提出了一种分布式领导-跟随容错跟踪控制方案。神经网络为基础的自适应学习算法的开发，学习未知的故障功能，保证系统的稳定性和合作跟踪，即使在存在多个并发过程和执行器故障的分布式代理。时变的领导者的命令只传达给一小部分的追随者代理通过有向链路，每个追随者代理交换本地测量信息，只有通过一个双向的，但不对称的拓扑结构与它的邻居。自适应容错算法是针对两种情况开发的，即，分别具有全状态测量和仅具有有限输出测量。在一定的假设下，严格建立了闭环稳定性和渐近的领导者-追随者跟踪性质。
This paper focuses on developing a distributed leader-following fault-tolerant tracking control scheme for a class of high-order nonlinear uncertain multiagent systems. Neural network-based adaptive learning algorithms are developed to learn unknown fault functions, guaranteeing the system stability and cooperative tracking even in the presence of multiple simultaneous process and actuator faults in the distributed agents. The time-varying leader’s command is only communicated to a small portion of follower agents through directed links, and each follower agent exchanges local measurement information only with its neighbors through a bidirectional but asymmetric topology. Adaptive fault-tolerant algorithms are developed for two cases, i.e., with full-state measurement and with only limited output measurement, respectively. Under certain assumptions, the closed-loop stability and asymptotic leader-follower tracking properties are rigorously established.