Genetic synthesis of fuzzy logic controllers in turning

The paper presents an optimal fuzzy logic controller design using efficient robust optimization techniques called genetic algorithms. It is shown that genetic algorithms can automatically search input and output scaling factors, membership functions, and fuzzy rules of the fuzzy logic controllers based on a fitness function. As a result, fuzzy logic controllers with optimal control performance can be systematically constructed instead of using a time-consuming trial and error approach. An adaptive force control system in turning operations is then used to illustrate the proposed method. It is shown that the developed fuzzy logic controller can achieve an automatic adjustment of feed rate to optimize the production rate with a constant cutting force in turning operations.