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Optimazation of Simulated Fuzzy Car Controller Using Genetic Algorithm  

Kim Bong-Gi (진주산업대학교 컴퓨터공학부)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.10, no.1, 2006 , pp. 212-219 More about this Journal
Abstract
The important problem in designing a Fuzzy Logic Controller(FLC) is generation of fuzzy control rules and it is usually the case that they are given by human experts of the problem domain. However, it is difficult to find an well-trained expert to any given problem. In this paper, I describes an application of genetic algorithm, a well-known global search algorithm to automatic generation of fuzzy control rules for FLC design. Fuzzy rules are automatically generated by evolving initially given fuzzy rules and membership functions associated fuzzy linguistic terms. Using genetic algorithm efficient fuzzy rules can be generated without any prior knowledge about the domain problem. In addition expert knowledge can be easily incorporated into rule generation for performance enhancement. We experimented genetic algorithm with a non-trivial vehicle controling problem. Our experimental results showed that genetic algorithm is efficient for designing any complex control system and the resulting system is robust.
Keywords
Genetic algorithm; Fuzzy car controller; Optimization;
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1 Chuen Chien Lee, 'Fuzzy Logic Control Systems: Fuzzy Logic Controller-Part I', IEEE Transactions on Systems, Man, and Cybernetics, vol. 20, No. 2, 1990
2 F. Hoffmann, et al., Evolutionary Learning of Mobile Robot Behaviours, First Workshop on Frontiers in Evolutionary Algorithms, 1997
3 윤철민, '유전자 알고리즘을 이용한 제어를 위한 퍼지 규칙 생성', 숭실대학교 대학원 전자계산학과 석사 학위 논문, 1998
4 Z. Michalewicz, 'Genetic.Algorithms + Data Structures = Evolution Programs', Third, Extended Edition, Springer-Verlag, 1995
5 Potter M. A., De Jong K., 'A Cooperative Coevolutionary Approach to Function Optimization', Parallel Problem Solving from Nature-PPSN III, pp.249-257, 1994
6 장경익, 박성진, 김명원, '진화 알고리즘을 이용한 퍼지 논리 제어기의 최적화' 99년 인공지능 퍼지 시스템학회 종합 학술대회, 1999
7 Tomonori HASHIYAMA, Takeshi FURUHASHI, Yoshiki UCHIKAWA, 'A Study on Finding Fuzzy Rules for Semi-Active Suspension Controllers with Genetic Algorithm', First Online Workshop on Evolutionary Computation, 1995
8 Frank Hoffmann, Gerd Pfister, 'Evolutionary Design of a Fuzzy Knowledge Base for a Mobile Robot', International Journal of Approximate Reasoning ,vol. 17, no. 4, pp.447-469, (1997)   DOI   ScienceOn
9 Schin-ichi Horikawa et al., 'A Fuzzy Controller Using a Neural Network and Its Capability to Learn Control Rules,' Proc. of the International Conference on Fuzzy Logic & Neural Networks, pp.103-106, 1990
10 Hasegawa T., Horikawa S.-i. Furuhashi T., Uchikawa Y., 'On design of adaptive fuzzy controller using fuzzy neural networks and a description of its dynamical behavior', Fuzzy Sets And Systems, Vol. 71, Issue 1, pp.3-23, 1995   DOI   ScienceOn
11 Leonid REZNIK, 'Evolution of Fuzzy Controller Design', FUZZ IEEE, 1997