Design of sophisticated fuzzy logic controllers using genetic algorithms

Abstract

A b s h c t -Design of fuzzy logic controllers encounters difficulties in the selection of optimized membership functions and fuzzy rule base, which is traditionally achieved by a tedious trial-and error process. This paper develops genetic algorithms for automatic design of high performance fuzzy logic controllers using sophisticated membership functions that intrinsically reflect the nonlinearities encounter in many engineering control applications. The controller design space is coded in base7 strings (chromosomes), where each bit (gene) matches the 7 discrete fuzzy value. The developed approach is subsequently applied to design of a proportional plus integral type fuzzy controller for a nonlinear water level control system. The performance of this control system is demonstrated higher than that of a conventional PID controller.For further comparison, a fuzzy proportional plus derivative controller is also developed using this approach, the response of which is shown to present no steady-state error.

Design of a Fuzzy Sliding Mode Controller by Genetic Algorithms for Induction Machine Speed Control

“…Different approaches have been proposed to automate the design of fuzzy systems [3,13,14]. Many of these approaches take the genetic algorithm as a base of the learning process.…”

“…Many of these approaches take the genetic algorithm as a base of the learning process. A GA was used to optimize the fuzzy logic input membership functions, the fuzzy rules, the output membership functions and universe of discourse [3,4].…”

“…In order to improve the performance of fuzzy sliding mode controller, we try to adjust the parameters of input and output membership functions and rule base of the FLC so we adjust indirectly φ and k in the control law. We use GA to search the appropriate values of the parameters of the FLC [3,11].…”

“…Fuzzy logic control (FLC) has excelled in dealing with systems that are complex, ill-defined, non-linear, or time-varying [3]. FLC is relatively easy to implement, as it usually needs no mathematical model of the controlled system.…”

“…Second, the system has to be known precisely. The fuzzy controller is weaker in stability because it lacks a strict mathematics model to demonstrate, although many researches show that it can be stabilized anyway [3,4]. Nevertheless, the concept of a sliding mode controller (SMC) can be employed to be a basis to ensure the stability of the controller.…”