Optimization of a fuzzy logic controller using genetic algorithms

The design of a fuzzy controller suffers from choice problems of fuzzy input and output membership functions and rules inference system definition. Generally, such procedures are implemented by trial and error iterations which do not assure an optimal fuzzy controller design. Moreover the fuzzy features of control system depend by the specific application of fuzzy controller. There are several techniques reported in recent literature that use Genetic Algorithms to optimize a fuzzy logic controller. This paper proposes a methodology to optimize fuzzy logic parameters based on Genetic Algorithms. The scheme is applied to the problem of electrical signal frequency driving for signals acquisition experiments. The fuzzy logic controller is tuned by Genetic Algorithms until to achieve the optimal parameters. The tuning design approach offers a complete and fast way to design an optimal fuzzy system. Moreover, the results show that the optimized fuzzy controller gives better performance than a conventional fuzzy controller also in terms of rise and settling time. © 2011 IEEE.