Optimal and Stable Fuzzy Controllers for Nonlinear Systems Based on an Improved Genetic Algorithm

Hung Fat Frank Leung, H. K. Lam, S. H. Ling, Peter K S Tam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

40 Citations (Scopus)

Abstract

This paper addresses the optimization and stabilization problems of nonlinear systems subject to parameter uncertainties. The methodology is based on a fuzzy logic approach and an improved genetic algorithm (GA). The TSK fuzzy plant model is employed to describe the dynamics of the uncertain nonlinear plant. A fuzzy controller is then obtained to close the feedback loop. The stability conditions are derived. The feedback gains of the fuzzy controller and the solution for meeting the stability conditions are determined using the improved GA. In order to obtain the optimal fuzzy controller, the membership functions are further tuned by minimizing a defined fitness function using the improved GA. An application example on stabilizing a two-link robot arm will be given.
Original languageEnglish
Pages (from-to)172-182
Number of pages11
JournalIEEE Transactions on Industrial Electronics
Volume51
Issue number1
DOIs
Publication statusPublished - 1 Feb 2004

Keywords

  • Fuzzy control
  • Nonlinear systems
  • Optimality and stability

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Instrumentation

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