Adaptive fuzzy logic controller blending the concepts of linguistic hedges and genetic algorithms

This paper describes a novel fuzzy logic controller, called linguistic hedge fuzzy logic controller, to simplify the membership function constructions and the rule developments. It is based on the concepts of the linguistic hedges and the genetic algorithms. The linguistic hedge operators adjust the shape of the system membership functions dynamically, and speed up the controller targeting its goal. The genetic algorithms search the optimal linguistic hedge combination in the linguistic hedge module. Accordingly, the linguistic hedge fuzzy logic controller has advantages: 1) it needs only the simple-shape membership functions for characterizing the related variables; 2) it is sufficient to adopt less number of rules for inference; 3) the rules are developed intuitionally without heavily depending on the endeavor of experts; 4) the linguistic hedge module associated with the genetic algorithm enables it to be adaptive; 5) it performs better than the conventional fuzzy logic controllers do; 6) it can be realized with low design complexity and small hardware overhead. Furthermore, the proposed approach has been applied to design two well-known nonlinear systems. The simulation results demonstrate the effectiveness of this design.