In this paper, an integration of fuzzy logic controllers (FLCs) with hybrid genetic algorithms (HGAs) is developed with a view to make the design process fully automatic, without requiring any human expert and numerical data. Our approach consists of two phases: first phase involves selection and definition of fuzzy control rules as well as adjustment of membership functions parameters, while the second phase performs an optimal selection of membership function types corresponding to fuzzy control rules. Learning both parts concurrently represents a way to improve the accuracy of the FLCs to minimize the errors. It has been argued that the performance of FLCs greatly depends on the parameters as well as types of membership functions. Thus, the aforementioned HGAs are a viable solution for designing an efficient adaptive FLCs system. To demonstrate the effectiveness of the proposed method for optimal design of the FLCs, the proposed approach is applied to a well-known benchmark controller design tasks, car and truck-and-trailer like robot system. Simulation results illustrates that proposed optimization approach can find optimal fuzzy rules and their corresponding membership functions types with a high rate of accuracy. The new HGAs optimized adaptive FLCs outperforms not only a passive control strategy but also human-designed FLCs, a neural coded controller with clustering and a neural-fuzzy control algorithm.
The Genetic Algorithms to build Fuzzy Logic Membership Functions
