TY - JOUR
T1 - Recurrent-fuzzy-neural-network-controlled linear induction motor servo drive using genetic algorithms
AU - Lin, Faa Jeng
AU - Huang, Po Kai
AU - Chou, Wen Der
N1 - Funding Information:
Manuscript received September 20, 2002; revised December 18, 2006. Abstract published on the Internet January 14, 2007. This work was supported by the National Science Council of Taiwan, R.O.C., under Grant NSC 90-2213-E-259-017.
PY - 2007/6
Y1 - 2007/6
N2 - A recurrent fuzzy neural network (RFNN) controller based on real-time genetic algorithms (GAs) is developed for a linear induction motor (LIM) servo drive in this paper. First, the dynamic model of an indirect field-oriented LIM servo drive is derived. Then, an online training RFNN with a backpropagation algorithm is introduced as the tracking controller. Moreover, to guarantee the global convergence of tracking error, a real-time GA is developed to search the optimal learning rates of the RFNN online. The GA-based RFNN control system is proposed to control the mover of the LIM for periodic motion. The theoretical analyses for the proposed GA-based RFNN controller are described in detail. Finally, simulated and experimental results show that the proposed controller provides high-performance dynamic characteristics and is robust with regard to plant parameter variations and external load disturbance.
AB - A recurrent fuzzy neural network (RFNN) controller based on real-time genetic algorithms (GAs) is developed for a linear induction motor (LIM) servo drive in this paper. First, the dynamic model of an indirect field-oriented LIM servo drive is derived. Then, an online training RFNN with a backpropagation algorithm is introduced as the tracking controller. Moreover, to guarantee the global convergence of tracking error, a real-time GA is developed to search the optimal learning rates of the RFNN online. The GA-based RFNN control system is proposed to control the mover of the LIM for periodic motion. The theoretical analyses for the proposed GA-based RFNN controller are described in detail. Finally, simulated and experimental results show that the proposed controller provides high-performance dynamic characteristics and is robust with regard to plant parameter variations and external load disturbance.
KW - Backpropagation algorithm
KW - Genetic algorithms (GAs)
KW - Linear induction motor (LIM)
KW - Recurrent fuzzy neural network (RFNN)
UR - http://www.scopus.com/inward/record.url?scp=40449104444&partnerID=8YFLogxK
U2 - 10.1109/TIE.2007.892256
DO - 10.1109/TIE.2007.892256
M3 - 期刊論文
AN - SCOPUS:40449104444
SN - 0278-0046
VL - 54
SP - 1449
EP - 1461
JO - IEEE Transactions on Industrial Electronics
JF - IEEE Transactions on Industrial Electronics
IS - 3
ER -