Adaptive neural networks-based fixed-time fault-tolerant consensus tracking for uncertain multiple Euler–Lagrange systems

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.