A GA-based fuzzy controller with sliding mode

In this study, the genetic algorithms are applied to find out a nearly optimal fuzzy rule-base for fuzzy sliding mode controller in the sense of fitness. In conventional fuzzy logic controllers (FLC), linearly increasing in either input variables or input linguistic labels would lead the number of rules grow up exponentially. Since the larger size of rule base would cause the longer string length and higher computing load, it becomes one of the difficulties of realizing genetic algorithms to search the suitable rules or membership functions for fuzzy logic controllers. This paper will show that the number of rules in fuzzy sliding mode controller (FSMC) is a linear function of input variables, such that the inferring load of the inference engine in FSMC is more light than that of FLC, and the string length of unknown parameters in FSMC is shorter than that in FLC. Therefore, using genetic algorithms to search fuzzy rules or membership functions for FSMC becomes more economical and applicable. The simulation results verify the efficiency of proposed approach