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JACIII Vol.15 No.6 pp. 639-651
doi: 10.20965/jaciii.2011.p0639
(2011)

Paper:

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Optimization of Fuzzy Logic Controller for Trajectory Tracking Using Genetic Algorithm

Pintu Chandra Shill*, M. A. H. Akhand**,
Md. Faijul Amin*, and Kazuyuki Murase*

*Department of System Design Engineering, Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, Japan

**Department of Computer Science and Engineering (CSE), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh

Received:
December 20, 2010
Accepted:
May 3, 2011
Published:
August 20, 2011
Creative Commons License
Keywords:
fuzzy logic controller, fuzzy rule base, genetic algorithm, genetic-fuzzy system, backing up a truck reversal problem
Abstract
Most Fuzzy Logic Controllers (FLCs) to date are working based on expert knowledge derived from heuristic knowledge of experienced operators. Conventional fuzzy logic controllers have poor adaptability due to invariable Membership Function (MF) parameters and fixed rule set. Conventional manual coded FLCs use only expert knowledge bases and do poorly with complex problems, especially with large numbers of input variables. We have developed FLCs using a Genetic Algorithm (GA) to automatically acquire knowledge that we call a genetic-fuzzy in which the GA is used to adaptively generate fuzzy rules and simultaneously selecting an appropriate MF shape. We also evaluate different membership functions in the fuzzy logic control. FLC sensibility is analysed and compared for different membership functions. We compare our proposed genetic-fuzzy approach to such existing methods, including as a manually coded conventional method, conventional method with complementary membership function, and a neuro-fuzzy method on a widely used test bed; backing up a truck reversal problem. Simulation results have shown our proposal to be superior to existing widely used methods.
Cite this article as:
P. Shill, M. Akhand, M. Amin, and K. Murase, “Optimization of Fuzzy Logic Controller for Trajectory Tracking Using Genetic Algorithm,” J. Adv. Comput. Intell. Intell. Inform., Vol.15 No.6, pp. 639-651, 2011.
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