Multiobjective fuzzy genetic algorithm optimisation approach to nonlinear control system design

Author

Trebi-Ollennu, A. ; White, B.A.

Author_Institution

Sch. of Eng. & Appl. Sci., Cranfield Univ. R. Mil. Coll. of Sci., Shrivenham, UK

Volume

144

Issue

2

fYear

1997

fDate

3/1/1997 12:00:00 AM

Firstpage

137

Lastpage

142

Abstract

Owing to the large number of free control parameters for modern nonlinear robust controllers, it is almost impossible to heuristically tune these parameters. The multiobjective fuzzy genetic algorithm optimisation is shown to provide an effective, efficient and intuitive framework for selecting these parameters. The control structure and specifications are assumed to be given. Using the concept of fuzzy sets and convex fuzzy decision making, a multiobjective fuzzy optimisation problem is formulated and solved using a genetic algorithm. The relative importance of the objective functions is assessed by using a new membership weighting strategy. The technique is applied to the selection of free control parameters for an input-output linearising controller with sliding mode control, in a remotely-operated underwater vehicle depth control system

Keywords

control system synthesis; fuzzy control; fuzzy set theory; genetic algorithms; nonlinear control systems; optimal control; robust control; I/O linearising controller; convex fuzzy decision-making; fuzzy sets; input-output linearising controller; membership weighting strategy; multiobjective fuzzy genetic algorithm optimisation; multiobjective fuzzy optimisation problem; nonlinear control system design; nonlinear robust controllers; remotely-operated underwater vehicle depth control system; sliding mode control;

fLanguage

English

Journal_Title

Control Theory and Applications, IEE Proceedings -

Publisher

iet

ISSN

1350-2379

Type

jour

DOI

10.1049/ip-cta:19971031

Filename

590901