Fuzzy controller design using genetic algorithms

Abstract

A methodology for combining genetic algorithms (GAs) with fuzzy controllers to create genetic/fuzzy controllers is presented. Using GAs, optimal or near optimal fuzzy rules and membership functions can be designed without a human operator's experience or a control engineer's knowledge, although such information can be used for the initial design. This genetic/fuzzy approach involves searching the encoded fuzzy rule and membership function parameter spaces using a fitness function that is defined in terms of a system performance criterion. We demonstrate this approach in an application where a GA adapts the fuzzy rules and membership functions of a fuzzy controller for a tracking system in real-time. The generalization ability of this tracking system is demonstrated by training it only on a step input, freezing its adaptable parameters, and then showing that it can accurately track other types of input signals.