Deng C, Yue D, Che WW, Xie X. Cooperative Fault-Tolerant Control for a Class of Nonlinear MASs by Resilient Learning Approach.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022;
PP:670-679. [PMID:
35675248 DOI:
10.1109/tnnls.2022.3176392]
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Abstract
In this article, a learning-based resilient fault-tolerant control method is proposed for a class of uncertain nonlinear multiagent systems (MASs) to enhance the security and reliability against denial-of-service (DoS) attacks and actuator faults. With the framework of cooperative output regulation, the developed algorithm consists of designing a distributed resilient observer and a decentralized fault-tolerant controller. Specifically, by using the data-driven method, an online resilient learning algorithm is first presented to learn the unknown exosystem matrix in the presence of DoS attacks. Then, a distributed resilient observer is proposed working against DoS attacks. In addition, based on the developed observer, a decentralized adaptive fault-tolerant controller is designed to compensate for actuator faults. Moreover, the convergence of error systems is shown by using the Lyapunov stability theory. The effectiveness of our result is examined by a simulation example.
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