Model reference adaptive fuzzy controller design using genetic algorithms

摘要

A methodology for combining genetic algorithms (GAs) with fuzzy algorithms to create fuzzy controlelrs for model reference adaptive control systems 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 useful for initial desings. 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 at the same time for a model reference adaptive tracking system.