Evolutionary algorithms for fuzzy control system design

This paper provides an overview on evolutionary learning methods for the au tomated design and optimization of fuzzy logic controllers. In a genetic tu ning process, an evolutionary algorithm adjusts the membership functions or scaling factors of a predefined fuzzy controller based on a performance in dex that specifies the desired control behavior Genetic learning processes are concerned with the automated design of the fuzzy rule base. Their objec tive is to generate a set of fuzzy if-then rules that establishes the appro priate mapping from input states to control actions. We describe two applic ations of genetic-fuzzy systems in detail: an evolution strategy that tunes the scaling and membership functions of a fuzzy cart-pole balancing contro ller and a genetic algorithm that learns the fuzzy control rules for an obs tacle-avoidance behavior of a mobile robot.