Multiobjective fuzzy genetic algorithm optimization approach to nonlinear control system design

Owing to the large number of free control parameters for modern nonlinear robust controllers it is almost impossible to heuristically tune these parameters. In this paper, multiobjective fuzzy genetic algorithm optimization is shown to provide all 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 optimization problem is formulated. The resulting optimization problem is solved using genetic algorithm. The relative importance of the objective functions are addressed using a new membership weighting strategy. The technique is applied to selecting the free control parameters for an input-output linearizing controller with sliding mode control for a remotely operated underwater vehicle depth control system.