TY - JOUR
T1 - Recurrent functional-link-based fuzzy neural network controller with improved particle swarm optimization for a linear synchronous motor drive
AU - Lin, Faa Jeng
AU - Chen, Syuan Yi
AU - Teng, Li Tao
AU - Chu, Hen
N1 - Funding Information:
This work was supported by the National Science Council of Taiwan, R.O.C. under Grant NSC 95-2221-E-008-177-MY3.
PY - 2009/8
Y1 - 2009/8
N2 - A recurrent functional link (FL)-based fuzzy neural network (FNN) controller is proposed in this study to control the mover of a permanent-magnet linear synchronous motor (PMLSM) servo drive to track periodic reference trajectories. First, the dynamic model of the PMLSM drive system is derived. Next, a recurrent FL-based FNN controller is proposed in this study to control the PMLSM. Moreover, the online learning algorithms of the connective weights, means, and standard deviations of the recurrent FL-based FNN are derived using the back-propagation (BP) method. However, divergence or degenerated responses will result from the inappropriate selection of large or small learning rates. Therefore, an improved particle swarm optimization (IPSO) is adopted to adapt the learning rates of the recurrent FL-based FNN online. Finally, the control performance of the proposed recurrent FL-based FNN controller with IPSO is verified by some simulated and experimental results.
AB - A recurrent functional link (FL)-based fuzzy neural network (FNN) controller is proposed in this study to control the mover of a permanent-magnet linear synchronous motor (PMLSM) servo drive to track periodic reference trajectories. First, the dynamic model of the PMLSM drive system is derived. Next, a recurrent FL-based FNN controller is proposed in this study to control the PMLSM. Moreover, the online learning algorithms of the connective weights, means, and standard deviations of the recurrent FL-based FNN are derived using the back-propagation (BP) method. However, divergence or degenerated responses will result from the inappropriate selection of large or small learning rates. Therefore, an improved particle swarm optimization (IPSO) is adopted to adapt the learning rates of the recurrent FL-based FNN online. Finally, the control performance of the proposed recurrent FL-based FNN controller with IPSO is verified by some simulated and experimental results.
KW - Functional link neural network (FLNN)
KW - Particle swarm optimization (PSO)
KW - Permanent-magnet linear synchronous motor (PMLSM)
UR - http://www.scopus.com/inward/record.url?scp=68549106126&partnerID=8YFLogxK
U2 - 10.1109/TMAG.2009.2017530
DO - 10.1109/TMAG.2009.2017530
M3 - 期刊論文
AN - SCOPUS:68549106126
SN - 0018-9464
VL - 45
SP - 3151
EP - 3165
JO - IEEE Transactions on Magnetics
JF - IEEE Transactions on Magnetics
IS - 8
M1 - 5170216
ER -