Finite-time adaptive neural control of uncertain constrained nonlinear systems with actuator fault

• Published in: Mechanical systems and signal processing Vol. 200; p. 110555
• Main Authors: Gao, Lihong, Song, Zhibao, Wang, Zhen, Li, Ping
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2023
• Subjects: Actuator fault; Finite-time stability; Neural networks; State-function constraints; Finite-time stability; Neural networks; State-function constraints; Actuator fault
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2023.110555

This article investigates the finite-time adaptive tracking control for a class of nonlinear systems, which are affected by unknown control coefficients, full state constraints of asymmetric functions and actuator fault. In order to overcome the full-state-function constraints, we present the barrier Lyapunov function (BLF) dependent of state and time. Meanwhile, the Nussbaum function and the radial basis function (RBF) neural networks (NNs) are introduced to handle unknown control coefficients and unknown nonlinear functions respectively. Then, a fault-tolerant tracking controller is established to pledge that all states do not violate the asymmetric state-function-constraint boundary, all signals of the closed-loop system are bounded, and the tracking error converges to a small neighborhood including the origin within finite time. The feasibility of the proposed control scheme is illustrated by two examples.