TY - JOUR
T1 - Fuzzy sliding-mode controlled induction motor servo drive based on real-time genetic algorithm
AU - Chou, Wen Der
AU - Lin, Faa Jeng
AU - Huang, Po Kai
N1 - Funding Information:
The author would like to acknowledge the financial support of the National Science Council of Taiwan, R.O.C. through its grant NSC 90-2213-E-259-017.
PY - 2004
Y1 - 2004
N2 - A genetic algorithm (GA)-based fuzzy sliding-mode control system is proposed in this study. The proposed controller combines the merits of the sliding-mode control, the fuzzy inference mechanism and the GA. First, a sliding-mode controller with an integral-operation switching surface is designed. Then, a fuzzy sliding-mode controller is investigated, in which a simple fuzzy inference mechanism is utilized to estimate the upper bound of uncertainties. Furthermore, the fuzzy inference mechanism with center adaptation of the membership functions is investigated to estimate the optimal bound of the uncertainties at the adaptive fuzzy sliding-mode controller. Since most of the parameters of the fuzzy inference mechanism are constants, optimal response usually cannot be obtained due to the existence of uncertainties. Therefore, a real-time GA with real-value coding is developed to search the optimal parameters of the fuzzy inference mechanism to improve the control performance. Finally, the position control of an induction motor (IM) servo drive using the proposed control strategy is illustrated.
AB - A genetic algorithm (GA)-based fuzzy sliding-mode control system is proposed in this study. The proposed controller combines the merits of the sliding-mode control, the fuzzy inference mechanism and the GA. First, a sliding-mode controller with an integral-operation switching surface is designed. Then, a fuzzy sliding-mode controller is investigated, in which a simple fuzzy inference mechanism is utilized to estimate the upper bound of uncertainties. Furthermore, the fuzzy inference mechanism with center adaptation of the membership functions is investigated to estimate the optimal bound of the uncertainties at the adaptive fuzzy sliding-mode controller. Since most of the parameters of the fuzzy inference mechanism are constants, optimal response usually cannot be obtained due to the existence of uncertainties. Therefore, a real-time GA with real-value coding is developed to search the optimal parameters of the fuzzy inference mechanism to improve the control performance. Finally, the position control of an induction motor (IM) servo drive using the proposed control strategy is illustrated.
KW - Adaptive fuzzy sliding-mode controller
KW - Genetic algorithm
KW - Induction motor servo drive
KW - Sliding-mode controller
UR - http://www.scopus.com/inward/record.url?scp=0345868842&partnerID=8YFLogxK
U2 - 10.1080/02533839.2004.9670847
DO - 10.1080/02533839.2004.9670847
M3 - 期刊論文
AN - SCOPUS:0345868842
SN - 0253-3839
VL - 27
SP - 35
EP - 47
JO - Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A/Chung-kuo Kung Ch'eng Hsuch K'an
JF - Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A/Chung-kuo Kung Ch'eng Hsuch K'an
IS - 1
ER -