Multiobjective optimization using variable complexity modelling for control system design

Authors:
Valceres V. R. Silva;Peter J. Fleming;Jungiro Sugimoto;Ryuichi Yokoyama
Affiliations:
Universidade Federal de São João del Rei (UFSJ), Praça Frei Orlando 170, 36307-352 São João del Rei, MG, Brazil;Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK;Department of Electrical Engineering, Tokyo Metropolitan University, 1-1 Minami Osawa, Hachioji-shi, Tokyo 192-0397, Japan;Department of Electrical Engineering, Tokyo Metropolitan University, 1-1 Minami Osawa, Hachioji-shi, Tokyo 192-0397, Japan
Venue:
Applied Soft Computing
Year:
2008

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Abstract

A multi-stage design approach that uses a multiobjective genetic algorithm as the framework for optimization and multiobjective preference articulation, and an H_infty loop-shaping technique are used to design controllers for a gas turbine engine. A non-linear model is used to assess performance of the controller. Because the computational load of applying multiobjective genetic algorithm to this control strategy is very high, a neural network and response surface models are used in order to speed up the design process within the framework of a multiobjective genetic algorithm. The final designs are checked using the original non-linear model.