Fuzzy logic controller(FLC) is one of very promising application areas of fuzzy logic and is being studied extensively. The important problem in designing an FLC is generation of fuzzy control rules and it is usually the case that they are given by human experts of the problem domain. However, it is difficult to find an well-trained expert to any given problem. To a non-expert designing a controller often causes problems such as wasted time and money involved in many trial-and-errors, generation of incomplete and inefficient rules, and lacking adaptability of FLC. In this paper, we describe an application of genetic algorithm, a well-known global search algorithm to automatic generation of fuzzy control rules for FLC design. Fuzzy rules are automatically generated by evolving initially given fuzzy rules and membership functions associated fuzzy linguistic terms. Using genetic algorithm efficient fuzzy rules can be generated without any prior knowledge about the domain problem. In addition expert knowledge can be easily incorporated into rule generation for performance enhancement.
We experimented genetic algorithm with a non-trivial vehicle controling problem. Given a set of values corresponding to input parameters such as off-road distance, directional angle, current speed, and road curvature, the controller generates values for control parameters such as acceleration and steering angle of the target vehicle. Our experimental results showed that genetic algorithm is efficient for designing any complex control system and the resulting system is robust.