An ISS-modular approach for adaptive neural control of pure-feedback systems

  • Authors:

  • Cong Wang;David J. Hill;S. S. Ge;Guanrong Chen

  • Affiliations:

  • College of Automation, South China University of Technology, Guangzhou 510641, China;Research School of Information Sciences and Engineering, The Australian National University, Australia;Department of Electrical and Computer Engineering, The National University of Singapore, Singapore;Department of Electronic Engineering, City University of Hong Kong, Hong Kong SAR, China

  • Venue:
  • Automatica (Journal of IFAC)
  • Year:
  • 2006

Quantified Score

Hi-index 22.15

Visualization

Abstract

Controlling non-affine non-linear systems is a challenging problem in control theory. In this paper, we consider adaptive neural control of a completely non-affine pure-feedback system using radial basis function (RBF) neural networks (NN). An ISS-modular approach is presented by combining adaptive neural design with the backstepping method, input-to-state stability (ISS) analysis and the small-gain theorem. The difficulty in controlling the non-affine pure-feedback system is overcome by achieving the so-called ''ISS-modularity'' of the controller-estimator. Specifically, a neural controller is designed to achieve ISS for the state error subsystem with respect to the neural weight estimation errors, and a neural weight estimator is designed to achieve ISS for the weight estimation subsystem with respect to the system state errors. The stability of the entire closed-loop system is guaranteed by the small-gain theorem. The ISS-modular approach provides an effective way for controlling non-affine non-linear systems. Simulation studies are included to demonstrate the effectiveness of the proposed approach.