An optimised fuzzy logic controller

An optimal fuzzy logic controller (FLC) tuning algorithm is reported in this paper. With the aid of genetic algorithms (GA), optimal rules of a fuzzy logic controller are designed. This is achieved by deriving a tailor-made encoding scheme, initialisation, crossover and mutation of rule table into chromosomes. The genetic algorithm incorporates as much existing knowledge of the system as possible to increase the speed of optimisation. For a linear system, the proposed algorithm shows a significant improvement in convergence. For a nonlinear system, the algorithm attempted the truck and truck-and-tailer backing up problems for the whole plan. It is concluded that the proposed algorithm can be applied effectively to solve variety of problems and can accommodate different performance criterion.

[1]  Dr. Hans Hellendoorn,et al.  An Introduction to Fuzzy Control , 1996, Springer Berlin Heidelberg.

[2]  Lalit M. Patnaik,et al.  Genetic algorithms: a survey , 1994, Computer.

[3]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[4]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[5]  W. E. Thompson,et al.  Design of intelligent fuzzy logic controllers using genetic algorithms , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[6]  Masoud Mohammadian,et al.  Tuning and optimisation of membership functions of fuzzy logic controllers by genetic algorithms , 1994, Proceedings of 1994 3rd IEEE International Workshop on Robot and Human Communication.

[7]  K. C. Ng,et al.  Design of sophisticated fuzzy logic controllers using genetic algorithms , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[8]  Masoud Mohammadian,et al.  Generating fuzzy rules by genetic algorithms , 1994, Proceedings of 1994 3rd IEEE International Workshop on Robot and Human Communication.

[9]  Bart Kosko,et al.  Adaptive fuzzy systems for backing up a truck-and-trailer , 1992, IEEE Trans. Neural Networks.

[10]  Michio Sugeno,et al.  Industrial Applications of Fuzzy Control , 1985 .

[11]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[12]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .