DocumentCode :
1778022
Title :
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
Link To Document :
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