A new robust observer-based adaptive type-2 fuzzy control for a class of nonlinear systems


Mohammadzadeh A., Hashemzadeh F.

Applied Soft Computing Journal, vol.37, pp.204-216, 2015 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 37
  • Publication Date: 2015
  • Doi Number: 10.1016/j.asoc.2015.07.036
  • Journal Name: Applied Soft Computing Journal
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.204-216
  • Keywords: Adaptive compensator, Approximation error, Indirect adaptive control, Observer, Robust, Simplified interval type-2 fuzzy neural network
  • Istanbul Technical University Affiliated: No

Abstract

In this paper, a novel robust observer-based adaptive controller is presented using a proposed simplified type-2 fuzzy neural network (ST2FNN) and a new three dimensional type-2 membership function is presented. Proposed controller can be applied to the control of high-order nonlinear systems and adaptation of the consequent parameters and stability analysis are carried out using Lyapunov theorem. Moreover, a new adaptive compensator is presented to eliminate the effect of the external disturbance, unknown nonlinear functions approximation errors and sate estimation errors. In the proposed scheme, using the Lyapunov and Barbalat's theorem it is shown that the system is stable and the tracking error of the system converges to zero asymptotically. The proposed method is simulated on a flexible joint robot, two-link robot manipulator and inverted double pendulums system. Simulation results confirm that in contrast to other robust techniques, our proposed method is simple, give better performance in the presence of noise, external disturbance and uncertainties, and has less computational cost.