@inproceedings{scholars1555,
         journal = {Proceedings of the International Conference on Power Electronics and Drive Systems},
           title = {Hybrid-fuzzy controller optimization via semi-parallel GA for servomotor control},
         address = {Singapore},
           pages = {51--56},
            note = {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},
            year = {2011},
             doi = {10.1109/PEDS.2011.6147223},
          author = {Saad, N. and Wahyunggoro, O. and Ibrahim, T.},
            isbn = {9781612849997},
        keywords = {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},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84857437011&doi=10.1109\%2fPEDS.2011.6147223&partnerID=40&md5=6cbb5d87615c1378467ec37602c14120},
        abstract = {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. {\^A}{\copyright} 2011 IEEE.}
}