Adaptive tracking control for a class of uncertain nonlinear systems with infinite number of actuator failures using neural networks

57Citations
Citations of this article
6Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

We consider adaptive compensation for infinite number of actuator failures in the tracking control of uncertain nonlinear systems. We construct an adaptive controller by combining the common Lyapunov function approach and the structural characteristic of neural networks. The proposed control strategy is feasible under the presupposition that the systems have a nonstrict-feedback structure. We prove that the states of the closed-loop system are bounded and the tracking error converges to a small neighborhood of the origin under the designed controllers, even though there are an infinite number of actuator failures. At last, the validity of the proposed control scheme is demonstrated by two examples.

Cite

CITATION STYLE

APA

Lv, W., & Wang, F. (2017). Adaptive tracking control for a class of uncertain nonlinear systems with infinite number of actuator failures using neural networks. Advances in Difference Equations, 2017(1). https://doi.org/10.1186/s13662-017-1426-5

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free