Multi-objective control design of the nonlinear systems using genetic algorithm

Author

Hajiloo, Amir ; Wen-Fang Xie

Author_Institution

Dept. of Mech. & Ind. Eng., Concordia Univ., Montreal, QC, Canada

fYear

2014

fDate

23-25 June 2014

Firstpage

27

Lastpage

34

Abstract

The problem of multi-objective feedback controller design of nonlinear systems is solved in this paper. The T-S fuzzy model is adopted to describe the nonlinear systems and genetic algorithm is used to identify the T-S fuzzy model. The identified T-S fuzzy model is reduced by applying Higher Order Singular Value Decomposition (HOSVD) method. Based on the reduced T-S fuzzy model, an optimal state feedback controller is designed by achieving the trade-off among three conflicting object functions using the optimal Pareto frontier. The simulation results reveal the effectiveness of the proposed method.

Keywords

Pareto optimisation; control system synthesis; fuzzy control; genetic algorithms; nonlinear control systems; singular value decomposition; state feedback; HOSVD method; T-S fuzzy model; Takagi-Sugeno fuzzy model; genetic algorithm; higher order singular value decomposition; multiobjective feedback controller design; nonlinear systems; object functions; optimal Pareto frontier; optimal state feedback controller; Computational modeling; Genetic algorithms; Linear programming; Mathematical model; Nonlinear systems; Optimization; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovations in Intelligent Systems and Applications (INISTA) Proceedings, 2014 IEEE International Symposium on

Conference_Location

Alberobello

Print_ISBN

978-1-4799-3019-7

Type

conf

DOI

10.1109/INISTA.2014.6873593

Filename

6873593