Stabilization of Nonlinear Nonminimum Phase Systems: Adaptive Parallel Approach Using Recurrent Fuzzy Neural Network

Ching Hung Lee*

*Corresponding author for this work

Research output: Contribution to journalArticle

41 Scopus citations

Abstract

In this paper, an adaptive parallel control architecture to stabilize a class of nonlinear systems which are nonminimum phase is proposed. For obtaining an on-line performance and self-tuning controller, the proposed control scheme contains recurrent fuzzy neural network (RFNN) identifier, nonfuzzy controller, and RFNN compensator. The nonfuzzy controller is designed for nominal system using the techniques of backstepping and feedback linearization, is the main part for stabilization. The RFNN compensator is used to compensate adaptively for the nonfuzzy controller, i.e., it acts like a fine tuner; and the RFNN identifier provides the system's sensitivity for tuning the controller parameters. Based on the Lyapunov approach, rigorous proofs are also presented to show the closed-loop stability of the proposed control architecture. With the aid of the RFNN compensators, the parallel controller can indeed improve system performance, reject disturbance, and enlarge the domain of attraction. Furthermore, computer simulations of several examples are given to illustrate the applicability and effectiveness of this proposed controller.

Original languageEnglish
Pages (from-to)1075-1088
Number of pages14
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume34
Issue number2
DOIs
StatePublished - Apr 2004

Keywords

  • Backstepping
  • Feedback linearization
  • Nonlinear control
  • Nonlinear nonminimum phase systems
  • Recurrent fuzzy neural network (RFNN)
  • System identification

Fingerprint Dive into the research topics of 'Stabilization of Nonlinear Nonminimum Phase Systems: Adaptive Parallel Approach Using Recurrent Fuzzy Neural Network'. Together they form a unique fingerprint.

Cite this