Design of self-learning fuzzy sliding mode controllers based on genetic algorithms

Abstract

In this paper, genetic algorithms were applied to search a sub-optimal fuzzy rule-base for a fuzzy sliding mode controller. Two types of fuzzy sliding mode controllers based on genetic algorithms were proposed. The fitness functions were defined so that the controllers which can drive and keep the state on the user-defined sliding surface would be assigned a higher fitness value. The sliding surface plays a very important role in the design of a fuzzy sliding mode controller. It can dominate the dynamic behaviors of the control system as well as reduce the size of the fuzzy rule-base. In conventional fuzzy logic control, an increase in either input variables or the associated linguistic labels would lead to the exponential growth of the number of rules. The number of parameters or the equivalent length of strings used in the computations of genetic algorithms for a fuzzy logic controller are usually quite extensive. As a result, the considerable computation load prevents the use of genetic operations in the tuning of membership functions in a fuzzy rule-base. This paper shows that the number of rules in a fuzzy sliding mode controller is a linear function of the number of input variables. The computation load of the inference engine in a fuzzy sliding mode controller is thus smaller than that in a fuzzy logic controller. Moreover, the string length of parameters is shorter in a fuzzy sliding mode controller than in a fuzzy logic controller when the parameters are searched by genetic algorithms. The simulation results showed the efficiency of the proposed approach and demonstrated the applicability of the genetic algorithm in the fuzzy sliding mode controller design.