%0 Conference Paper
%A Saad, N.
%A Wahyunggoro, O.
%A Ibrahim, T.
%D 2011
%F scholars:1555
%K Control modes; Controller optimization; Defuzzifiers; Feedback controller; Fuzzifications; Input and outputs; Integral controllers; Modified genetic algorithms; Performance comparison; Scale Factor; Servo motor control; SPOGA; Test runs, Controllers; Optimization; Power electronics; Servomotors, Genetic algorithms
%P 51-56
%R 10.1109/PEDS.2011.6147223
%T Hybrid-fuzzy controller optimization via semi-parallel GA for servomotor control
%U https://khub.utp.edu.my/scholars/1555/
%X 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. © 2011 IEEE.
%Z cited By 1; Conference of 2011 IEEE 9th International Conference on Power Electronics and Drive Systems, PEDS 2011 ; Conference Date: 5 December 2011 Through 8 December 2011; Conference Code:88711