Fuzzy Process Control with a Genetic Algorithm

The U.S. Bureau of Mines is currently investigating ways to combine the learning capabilities of genetic algorithms with the process control capabilities of fuzzy logic. Fuzzy logic has been successfully used for controlling a number of physical systems. However, the selection of acceptable fuzzy membership functions has generally been a subjective decision. In this paper, high-performance fuzzy membership functions are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the genetic algorithm provide for a more efficient fuzzy logic controller than the membership functions selected by the authors for the liquid level system considered. The approach to developing genetic algorithm-based fuzzy logic controllers is demonstrated on a liquid level controller.