TY - JOUR
T1 - Adaptive fuzzy-neural-network control for a DSP-based permanent magnet linear synchronous motor servo drive
AU - Lin, Faa Jeng
AU - Shen, Po Hung
N1 - Funding Information:
Manuscript received October 14, 2003; revised May 22, 2005 and October 22, 2005. This paper was supported by the National Science Council of Taiwan, R.O.C. under Grant NSC 91-2213-E-259-021. The authors are with the Department of Electrical Engineering, National Dong Hwa University, Hualien 974, Taiwan (e-mail: [email protected]). Digital Object Identifier 10.1109/TFUZZ.2006.876744
PY - 2006/8
Y1 - 2006/8
N2 - An adaptive fuzzy neural network (AFNN) control system is proposed to control the position of the mover of a field-oriented control permanent magnet linear synchronous motor (PMLSM) servo-drive system to track periodic reference trajectories in this paper. In the proposed AFNN control system, an FNN with accurate approximation capability is employed to approximate the unknown dynamics of the PMLSM, and a robust compensator is proposed to confront the inevitable approximation errors due to finite number of membership functions and disturbances including the friction force. The adaptive learning algorithm that can learn the parameters of the FNN on line is derived using Lyapunov stability theorem. Moreover, to relax the requirement for the value of lumped uncertainty in the robust compensator, which comprises a minimum approximation error, optimal parameter vectors, higher order terms in Taylor series and friction force, an adaptive lumped uncertainty estimation law is investigated. Furthermore, all the control algorithms are implemented in a TMS320C32 DSP-based control computer. The simulated and experimental results due to periodic reference trajectories show that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties.
AB - An adaptive fuzzy neural network (AFNN) control system is proposed to control the position of the mover of a field-oriented control permanent magnet linear synchronous motor (PMLSM) servo-drive system to track periodic reference trajectories in this paper. In the proposed AFNN control system, an FNN with accurate approximation capability is employed to approximate the unknown dynamics of the PMLSM, and a robust compensator is proposed to confront the inevitable approximation errors due to finite number of membership functions and disturbances including the friction force. The adaptive learning algorithm that can learn the parameters of the FNN on line is derived using Lyapunov stability theorem. Moreover, to relax the requirement for the value of lumped uncertainty in the robust compensator, which comprises a minimum approximation error, optimal parameter vectors, higher order terms in Taylor series and friction force, an adaptive lumped uncertainty estimation law is investigated. Furthermore, all the control algorithms are implemented in a TMS320C32 DSP-based control computer. The simulated and experimental results due to periodic reference trajectories show that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties.
KW - Adaptive lumped uncertainty estimation
KW - Fuzzy neural network (FNN)
KW - Permanent magnet linear synchronous motor (PMLSM)
KW - Taylor series
UR - http://www.scopus.com/inward/record.url?scp=33747605322&partnerID=8YFLogxK
U2 - 10.1109/TFUZZ.2006.876744
DO - 10.1109/TFUZZ.2006.876744
M3 - 期刊論文
AN - SCOPUS:33747605322
SN - 1063-6706
VL - 14
SP - 481
EP - 495
JO - IEEE Transactions on Fuzzy Systems
JF - IEEE Transactions on Fuzzy Systems
IS - 4
ER -