Neural networks-based adaptive finite-time control of switched nonlinear systems under time-varying actuator failures

• Main Authors: Xikui Liu, Xiurong Shi, Yan Li
• Format: Article
• Language: English
• Published: SpringerOpen 2019-11-01
• Series: Advances in Difference Equations
• Subjects: Finite-time tracking; Neural networks (NNs); Unknown actuation failures; Switched nonlinear systems
• Online Access: http://link.springer.com/article/10.1186/s13662-019-2396-6

Abstract This paper is dedicated to neural networks-based adaptive finite-time control design of switched nonlinear systems in the time-varying domain. More specifically, by employing the approximation ability of neural networks system, an integrated adaptive controller is constructed. The main aim is to make sure the closed-loop system in arbitrary switching signals is semi-global practical finite-time stable (SGPFS). A backstepping design with a common Lyapunov function is proposed. Unlike some existing control schemes with actuator failures, the key is dealing with the time-varying fault-tolerant job for the switched system. It is also proved that all signals in the system are bounded and the tracking error can converge in a small field of the origin in finite time. A practical example is presented to illustrate the validity of the theory.