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Hybrid-fuzzy controller optimization via semi-parallel GA for servomotor control

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3 Author(s)
Saad, N. ; Dept. of Electr. & Electron. Eng., Univ. Teknol. Petronas, Tronoh, Malaysia ; Wahyunggoro, O. ; Ibrahim, T.

Servomotor uses feedback controller to control either the speed or the position or both. This paper discusses the performance comparisons of a modified genetic algorithm, named as the semi-parallel operation genetic algorithm (SPOGA) and the conventional genetic algorithm (GA), in optimizing the I/O scale factors, membership functions, and rules of a hybrid-fuzzy controller. Singleton fuzzification is used as a fuzzifier with seven membership functions for both input and output of the controller, whilst center of average is used as a defuzzifier. A 21-bit-30-population is used in SPOGA for both I/O scales and for membership functions. Two control modes are applied in cascaded: position and speed. Both the simulation and practical experiment results show that fuzzy-logic parallel integral controller (FLIC) with SPOGA-optimized is better as compared to FLIC with GA-optimized and also the non-optimized FLIC, FLC, and PI in terms of performance and the reduction of the number of test runs for the optimization.

Published in:

Power Electronics and Drive Systems (PEDS), 2011 IEEE Ninth International Conference on

Date of Conference:

5-8 Dec. 2011