A direct self-constructing neural controller design for a class of nonlinear systems

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Article number7045554
Pages (from-to)1312-1322
Journal / PublicationIEEE Transactions on Neural Networks and Learning Systems
Volume26
Issue number6
Online published19 Feb 2015
Publication statusPublished - Jun 2015

Abstract

This paper is concerned with the problem of adaptive neural control for a class of uncertain or ill-defined nonaffine nonlinear systems. Using a self-organizing radial basis function neural network (RBFNN), a direct self-constructing neural controller (DSNC) is designed so that unknown nonlinearities can be approximated and the closed-loop system is stable. The key features of the proposed DSNC design scheme can be summarized as follows. First, different from the existing results in literature, a self-organizing RBFNN with adaptive threshold is constructed online for DSNC to improve the control performance. Second, the control law and adaptive law for the weights of RBFNN are established so that the closed-loop system is stable in the term of Lyapunov stability theory. Third, the tracking error is guaranteed to uniformly asymptotically converge to zero with the aid of an additional robustifying control term. An example is finally given to demonstrate the design procedure and the performance of the proposed method. Simulation results reveal the effectiveness of the proposed method.

Research Area(s)

  • Adaptive control, Asymptotically stability, Neural networks (NNs), Nonlinear systems, Self-organizing

Citation Format(s)

A direct self-constructing neural controller design for a class of nonlinear systems. / Han, Honggui; Zhou, Wendong; Qiao, Junfei et al.
In: IEEE Transactions on Neural Networks and Learning Systems, Vol. 26, No. 6, 7045554, 06.2015, p. 1312-1322.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review