Khalili Mohsen, Zhang Xiaodong, Cao Yongcan, Polycarpou Marios M, Parisini Thomas
IEEE Trans Neural Netw Learn Syst. 2020 Feb;31(2):420-432. doi: 10.1109/TNNLS.2019.2904277. Epub 2019 Apr 11.
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.
本文致力于为一类高阶非线性不确定多智能体系统开发一种分布式领导者-跟随者容错跟踪控制方案。基于神经网络的自适应学习算法被开发出来以学习未知的故障函数，即使在分布式智能体中同时存在多个过程和执行器故障的情况下，也能保证系统的稳定性和协同跟踪。时变领导者的指令仅通过有向链路传达给一小部分跟随智能体，并且每个跟随智能体仅通过双向但不对称的拓扑结构与其邻居交换局部测量信息。分别针对两种情况开发了自适应容错算法，即全状态测量情况和仅有限输出测量情况。在某些假设下，严格建立了闭环稳定性和渐近领导者-跟随者跟踪特性。
