Affordable Access

Publisher Website

Indirect adaptive control of nonlinear dynamic systems using self recurrent wavelet neural networks via adaptive learning rates

Authors
Journal
Information Sciences
0020-0255
Publisher
Elsevier
Publication Date
Volume
177
Issue
15
Identifiers
DOI: 10.1016/j.ins.2007.02.009
Keywords
  • Indirect Adaptive Control
  • Self Recurrent Wavelet Neural Network
  • Adaptive Learning Rate
  • Intelligent Control
  • Dynamic System
Disciplines
  • Computer Science
  • Mathematics

Abstract

Abstract This paper proposes an indirect adaptive control method using self recurrent wavelet neural networks (SRWNNs) for dynamic systems. The architecture of the SRWNN is a modified model of the wavelet neural network (WNN). However, unlike the WNN, since a mother wavelet layer of the SRWNN is composed of self-feedback neurons, the SRWNN can store the past information of wavelets. In the proposed control architecture, two SRWNNs are used as both an identifier and a controller. The SRWNN identifier approximates dynamic systems and provides the SRWNN controller with information about the system sensitivity. The gradient-descent method using adaptive learning rates (ALRs) is applied to train all weights of the SRWNN. The ALRs are derived from discrete Lyapunov stability theorem, which are applied to guarantee the convergence of the proposed control system. Finally, we perform some simulations to verify the effectiveness of the proposed control scheme.

There are no comments yet on this publication. Be the first to share your thoughts.