Fuzzy controller design by parallel genetic algorithms

G. Mondelli; G. Castellano; Giovanni Attolico; Arcangelo Distante

doi:10.1117/12.304823

25 March 1998 Fuzzy controller design by parallel genetic algorithms

G. Mondelli, G. Castellano, Giovanni Attolico, Arcangelo Distante

Proceedings Volume 3390, Applications and Science of Computational Intelligence; (1998) https://doi.org/10.1117/12.304823
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

Abstract

Designing a fuzzy system involves defining membership functions and constructing rules. Carrying out these two steps manually often results in a poorly performing system. Genetic Algorithms (GAs) has proved to be a useful tool for designing optimal fuzzy controller. In order to increase the efficiency and effectiveness of their application, parallel GAs (PAGs), evolving synchronously several populations with different balances between exploration and exploitation, have been implemented using a SIMD machine (APE100/Quadrics). The parameters to be identified are coded in such a way that the algorithm implicitly provides a compact fuzzy controller, by finding only necessary rules and removing useless inputs from them. Early results, working on a fuzzy controller implementing the wall-following task for a real vehicle as a test case, provided better fitness values in less generations with respect to previous experiments made using a sequential implementation of GAs.

Citation Download Citation

G. Mondelli, G. Castellano, Giovanni Attolico, and Arcangelo Distante "Fuzzy controller design by parallel genetic algorithms", Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); https://doi.org/10.1117/12.304823

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