Adaptive neural networks-based fixed-time fault-tolerant consensus tracking for uncertain multiple Euler–Lagrange systems. (October 2022) - Li, He; Liu, Cheng-Lin; Zhang, Ya; Chen, Yang-Yang
Abstract: This article addresses the fixed-time fault-tolerant consensus tracking (FTCT) problem for uncertain multiple Euler–Lagrange systems (MELS) with the digraph and actuator faults. Firstly, a fixed-time distributed observer (DO) is built to estimate the states of leader. Then, the approximation ability of radical basic function neural networks (RBFNN) is exploited to deal with the system uncertainties. By using backstepping technique, the novel fault-tolerant local control protocol (FTLCP) and updating laws are designed to ensure that error variables converge to the small adjacent area of zero within fixed-time. Eventually, the effectiveness and practicality of the presented method are demonstrated through a typical MELS simulation. Highlights: For multiple Euler–Lagrange systems, practical factors such as asymmetry of directed communication networks, system uncertainties and actuator faults are comprehensively considered under the fixed-time convergence framework, which greatly expand the application scope of this article. A novel distributed observer of multiple Euler–Lagrange systems under the digraph is proposed. Furthermore, the estimation errors can converge to the origin within fixed time, and the estimation of the upper bound of setting time is relatively less conservative. A novel and effective adaptive fault-tolerant local control protocol is proposed to ensure the tracking errors converge into a compact set near zero in fixed-time. The constructed distributed observer based local tracking control structure significantly reduces the difficulty of control protocol design and stability analysis.