A Genetic Approach for Simultaneous Design of Membership Functions and Fuzzy Control Rules

Authors:
Chia-Ju Wu;Guan-Yu Liu
Affiliations:
Department of Electrical Engineering, National Yunlin University of Science and Technology, Touliu, Yunlin 640, Taiwan, R.O.C./ e-mail: wucj@pine. yuntech.edu.tw;Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Touliu, Yunlin 640, Taiwan, R.O.C.
Venue:
Journal of Intelligent and Robotic Systems
Year:
2000

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Abstract

Based on the genetic algorithm (GA), an approach is proposed for simultaneous design of membership functions and fuzzy control rules since these two components are interdependent in designing a fuzzy logic controller (FLC). With triangular membership functions, the left and right widths of these functions, the locations of their peaks, and the fuzzy control rules corresponding to every possible combination of input linguistic variables are chosen as parameters to be optimized. By using a proportional scaling method, these parameters are then transformed into real-coded chromosomes, over which the offspring are generated by rank-based reproduction, convex crossover, and nonuniform mutation. Meanwhile, the concept of enlarged sampling space is used to expedite the convergence of the evolutionary process. To show the feasibility and validity of the proposed method, a cart-centering example will be given. The simulation results will show that the designed FLC can drive the cart system from any given initial state to the desired final state even when the cart mass varies within a wide range.