Differential Evolution Based Fuzzy Logic Controller for Nonlinear Process Control

Abstract

This paper presents an unconventional approach to adaptive fuzzy logic controller (FLC) design wherein a new evolution strategy, Differential Evolution (DE) is used in the simultaneous design of membership functions and rule sets for fuzzy logic controllers. Differential Evolution is an exceptionally simple, fast, and robust population based search algorithm that is able to locate near-optimal solutions to difficult problems. This technique, which is similar to genetic algorithms, has been applied to the control of pH, which is a requirement in many chemical industries. Control of pH poses a difficult problem because of inherent nonlinearities and frequently changing process dynamics. This technique has been successfully implemented on a laboratory scale pH plant setup. The results have been compared with a simple GA based adaptive FLC where we have incorporated a search space smoothing function for achieving faster convergence and for ascertaining a global optimum. Results indicate that FLC’s augmented with DE’s offer a powerful alternative to GA based FLC’s. Results also show that the search space smoothing function helps in faster convergence of a GA.