Design of Fuzzy Logic Controllers Based on Evolvable Hardware Platform

Fuzzy Logic Controllers (FLCS) are rule-based system that successfully incorporate the flexibility of human-decision making by means of the use of fuzzy set theory. This paper provides an overview on evolutionary learning methods for the automated design and optimization of fuzzy logic controllers. A three-stage evolution framework that uses Genetic Programming (GP) and Genetic Algorithms (GAS) evolves rule-base and membership function parameters of FLCS. For hardware implement of FLCS, We propose an Evolvable Hardware (EHW) platform for the design of fuzzy logic controllers. A simulation experiment is shown though this hybrid model in the design of fuzzy control systems. In the experiment, membership functions of input and output variables are defined by three parameters, called geometric proportional factors, adding with the scaling factors, which are adjusted to maximize the performance index by using the Genetic Algorithm. The fuzzy controller is designed to be more capability.