A novel scheme for the design of backstepping control for a class of nonlinear systems

21Citations
Citations of this article
11Readers
Mendeley users who have this article in their library.

Abstract

A novel scheme is proposed for the design of backstepping control for a class of state-feedback nonlinear systems. In the design, the unknown nonlinear functions are approximated by the neural networks (NNs) identification models. The Lyapunov function of every subsystem consists of the tracking error and the estimation errors of NN weight parameters. The adaptive gains are dynamically determined in a structural way instead of keeping them constants, which can guarantee system stability and parameter estimation convergence. When the modeling errors are available, the indirect backstepping control is proposed, which can guarantee the functional approximation error will converge to a rather small neighborhood of the minimax functional approximation error. When the modeling errors are not available, the direct backstepping control is proposed, where only the tracking error is necessary. The simulation results show the effectiveness of the proposed schemes. © 2010 Elsevier Inc.

Cite

CITATION STYLE

APA

Shi, H. (2011). A novel scheme for the design of backstepping control for a class of nonlinear systems. Applied Mathematical Modelling, 35(4), 1893–1903. https://doi.org/10.1016/j.apm.2010.10.018

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