Optimization of fuzzy logic controllers with rule base size reduction using genetic algorithms

In this paper, we present the automatic design methods with rule base size reduction for fuzzy logic controllers (FLCs). The adaptive schema is divided into two phases: the first phase is concerned with the adaptive learning method for optimizing the MFs parameters based on the binary coded genetic algorithms. The second phase is about the learning and reducing: automatically generate the fuzzy rules and at the same time apply the genetic reduction technique to determine the minimum number of fuzzy rules required in building the fuzzy models. In the rule base, the redundant rules are removed by setting their all consequents weight factor to zero and merging the conflicting rules during the learning process. The real and binary coded coupled genetic algorithms are applied for generating the optimal controllers that reduce the rule base size and optimal selection of fuzzy sets. Optimizing the MFs of FLCs with learning and reducing the number of fuzzy control rules concurrently represents a way to improve the computational efficiency and interpretability of FLCs to minimize the errors. The control algorithm is successfully tested for intelligent control of two degrees of freedom inverted pendulum. Finally, the simulation studies exhibits competing results with high accuracy that demonstrate the effective use of the proposed algorithm.