TY - GEN
T1 - Robust fuzzy-neural-network control for two-axis motion control system based on TMS320C32 control computer
AU - Lin, Faa Jeng
AU - Shen, Po Hung
PY - 2005
Y1 - 2005
N2 - In this study, a robust fuzzy-neural-network (RFNN) sliding-mode control based on computed-torque control design for a two-axis motion control system in which the X-Y table is composed of two permanent magnet linear synchronous motor (PMLSM) is proposed. 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 based on the derived motion dynamics. Moreover, the motions at X-axis and Y-axis are controlled separately. Using the proposed control, the motion tracking performance is significantly improved and the robustness to parameter variations, external disturbances, cross-coupled Interference and friction force can be obtained as well. Furthermore, all the control algorithms are implemented in a TMS320C32 DSP-based control computer. The experimental results due to circle and four leaves reference contours show that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties.
AB - In this study, a robust fuzzy-neural-network (RFNN) sliding-mode control based on computed-torque control design for a two-axis motion control system in which the X-Y table is composed of two permanent magnet linear synchronous motor (PMLSM) is proposed. 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 based on the derived motion dynamics. Moreover, the motions at X-axis and Y-axis are controlled separately. Using the proposed control, the motion tracking performance is significantly improved and the robustness to parameter variations, external disturbances, cross-coupled Interference and friction force can be obtained as well. Furthermore, all the control algorithms are implemented in a TMS320C32 DSP-based control computer. The experimental results due to circle and four leaves reference contours show that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties.
KW - Fuzzy-neural-network
KW - Permanent magnet linear synchronous motor
KW - Sliding-mode control
KW - X-Y table
UR - http://www.scopus.com/inward/record.url?scp=33748907002&partnerID=8YFLogxK
U2 - 10.1109/ICMECH.2005.1529328
DO - 10.1109/ICMECH.2005.1529328
M3 - 會議論文篇章
AN - SCOPUS:33748907002
SN - 0780389980
SN - 9780780389984
T3 - Proceedings of the 2005 IEEE International Conference on Mechatronics, ICM '05
SP - 606
EP - 610
BT - Proceedings of the 2005 IEEE International Conference on Mechatronics, ICM '05
T2 - 2005 IEEE International Conference on Mechatronics, ICM '05
Y2 - 10 July 2005 through 12 July 2005
ER -