Robust Adaptive Neural Backstepping Control for a Class of Nonlinear Systems with Dynamic Uncertainties

Hongyan Yang,Hamid Reza Karimi,Huanqing Wang

doi:10.1155/2014/658671

Hongyan Yang, Hamid Reza Karimi + Show 1 more

Open Access

https://doi.org/10.1155/2014/658671

Copy DOI

Abstract

This paper is concerned with adaptive neural control of nonlinear strict-feedback systems with nonlinear uncertainties, unmodeled dynamics, and dynamic disturbances. To overcome the difficulty from the unmodeled dynamics, a dynamic signal is introduced. Radical basis function (RBF) neural networks are employed to model the packaged unknown nonlinearities, and then an adaptive neural control approach is developed by using backstepping technique. The proposed controller guarantees semiglobal boundedness of all the signals in the closed-loop systems. A simulation example is given to show the effectiveness of the presented control scheme.

Highlights

In the past decades, much attention has been paid on the control design of complex nonlinear systems [1–11]
This paper focuses on the problem of adaptive neural control for nonlinear strictfeedback systems with unmodeled dynamics and dynamic disturbances
Compared with the control approaches [58, 59], the main contributions of this paper are summarized as follows: (1) the strict limitation to the dynamic disturbances is relaxed, which can refer to Remark 3; (2) by estimating the norm of the weight vector of neural networks basis functions, the number of adaptive parameters is not more than the order of the considered nonlinear system

Summary

Introduction

Much attention has been paid on the control design of complex nonlinear systems [1–11]. Approximation-based adaptive backstepping technique is an effective control approach for handling the control problem of highly uncertain complex nonlinear strict-feedback systems, in which neural networks or fuzzy systems are utilized to model the unknown nonlinear functions. The above adaptive neural or fuzzy backstepping control approaches required the controlled strict-feedback nonlinear systems to be free of the unmodeled dynamics and Abstract and Applied Analysis dynamic disturbances. Compared with the control approaches [58, 59], the main contributions of this paper are summarized as follows: (1) the strict limitation to the dynamic disturbances is relaxed, which can refer to Remark 3; (2) by estimating the norm of the weight vector of neural networks basis functions, the number of adaptive parameters is not more than the order of the considered nonlinear system.

Problem Formulation and Preliminaries

Adaptive Neural Control Design

Simulation Example

Findings

Conclusion