Neural network‐based decentralized adaptive fault‐tolerant control for a class of nonlinear interconnected systems with unknown input powers
Jiyu Zhu, Qikun Shen, Tianping Zhang
Abstract
Summary This article studies the output tracking control for a class of interconnected nonlinear systems with actuator faults, where each subsystem has unknown powers. Based on Lyapunov stability theory, a neural‐network‐based decentralized adaptive fault‐tolerant control scheme is proposed so that each tracking error can be constricted in a small adjustable neighborhood of the origin. In existing results, interconnections and actuator bias faults have been addressed. But the systems' input powers are required to be a known positive integer and preferably one. So in our article, we relax the restrictions and consider more general systems whose input powers are extended to larger than one and unknown at the same time. Finally, two numerical simulation examples are shown to explain the validity of control strategy proposed in our article.