Learning and tuning fuzzy logic controllers through genetic algorithm

Abstract

This paper reviews the current fuzzy control technology from the engineering point of view, and presents a new method for learning and tuning a fuzzy controller based on genetic algorithm (GA) for a dynamic system. In particular, it enhances the fuzzy controller with self-learning capability for achieving the prescribed control objective into near optimal manner. The methodology first adopts expert experiences, it then uses the GA to find the fuzzy controller's optimal set of parameters. In using GA, we must define an objective function to measure the performance of the controller. Since the behaviour of the dynamic system is hard to predict, a three-layer forward network has been adopted. For the purpose to accelerate the learning process, a conventional simplex optimal algorithm is used to reduce the search space. Finally, an example is given to show the potential of the method.<>