TY - JOUR
T1 - Robust fuzzy neural network sliding-mode control for two-axis motion control system
AU - Lin, Faa Jeng
AU - Shen, Po Hung
N1 - Funding Information:
Manuscript received April 15, 2004; revised June 3, 2005. Abstract published on the Internet May 18, 2006. This work 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, R.O.C. (e-mail: linfj@mail.ndhu.edu.tw). Digital Object Identifier 10.1109/TIE.2006.878312
PY - 2006/6
Y1 - 2006/6
N2 - A robust fuzzy neural network (RFNN) sliding-mode control based on computed torque control design for a two-axis motion control system is proposed in this paper. The two-axis motion control system is an x-y table composed of two permanent-magnet linear synchronous motors. First, a single-axis motion dynamics with the introduction of a lumped uncertainty including cross-coupled interference between the two-axis mechanism is derived. Then, to improve the control performance in reference contours tracking, the RFNN sliding-mode control system is proposed to effectively approximate the equivalent control of the sliding-mode control method. Moreover, the motions at x-axis and y-axis are controlled separately. Using the proposed control, the motion tracking performance is significantly improved, and robustness to parameter variations, external disturbances, cross-coupled interference, and friction force can be obtained as well. Furthermore, the proposed control algorithms are implemented in a TMS320C32 DSP-based control computer. From the simulated and experimental results due to circle and four leaves reference contours, the dynamic behaviors of the proposed control systems are robust with regard to uncertainties.
AB - A robust fuzzy neural network (RFNN) sliding-mode control based on computed torque control design for a two-axis motion control system is proposed in this paper. The two-axis motion control system is an x-y table composed of two permanent-magnet linear synchronous motors. First, a single-axis motion dynamics with the introduction of a lumped uncertainty including cross-coupled interference between the two-axis mechanism is derived. Then, to improve the control performance in reference contours tracking, the RFNN sliding-mode control system is proposed to effectively approximate the equivalent control of the sliding-mode control method. Moreover, the motions at x-axis and y-axis are controlled separately. Using the proposed control, the motion tracking performance is significantly improved, and robustness to parameter variations, external disturbances, cross-coupled interference, and friction force can be obtained as well. Furthermore, the proposed control algorithms are implemented in a TMS320C32 DSP-based control computer. From the simulated and experimental results due to circle and four leaves reference contours, the dynamic behaviors of the proposed control systems are robust with regard to uncertainties.
KW - Fuzzy neural network (FNN)
KW - Permanent-magnet linear synchronous motor (PMLSM)
KW - Sliding-mode control
KW - x-y table
UR - http://www.scopus.com/inward/record.url?scp=33747589450&partnerID=8YFLogxK
U2 - 10.1109/TIE.2006.878312
DO - 10.1109/TIE.2006.878312
M3 - 期刊論文
AN - SCOPUS:33747589450
VL - 53
SP - 1209
EP - 1225
JO - IEEE Transactions on Industrial Electronics
JF - IEEE Transactions on Industrial Electronics
SN - 0278-0046
IS - 4
ER -