TY - GEN
T1 - Deterministic Learning-based Generalizable Trajectory Tracking Control for Permanent-Magnet Synchronous Motors Driven Two-Axis X-Y Table
AU - Zhao, Zixian
AU - Fei, Yiming
AU - Han, Chengyu
AU - Li, Jiangang
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this paper, based on the deterministic learning theory and the model of permanent-magnet synchronous motors (PMSMs) driven two-axis X-Y table, a radial basis function neural network (RBFNN) learning control generalization rule for the non-repetitive trajectory tracking control of the system is proposed. Because of its excellent approximation capability, adaptability and learning capability for uncertain systems, RBFNN is used to approximate the model of X-Y table. Aiming to improve the generalization capability of deterministic learning theory, a generalization rule of deterministic learning theory for X-Y table is proposed. Based on the proposed generalizable deterministic learning control scheme, the tracking accuracy of the X-Y table is improved and the effectiveness of the generalization rules are verified through corresponding experiments.
AB - In this paper, based on the deterministic learning theory and the model of permanent-magnet synchronous motors (PMSMs) driven two-axis X-Y table, a radial basis function neural network (RBFNN) learning control generalization rule for the non-repetitive trajectory tracking control of the system is proposed. Because of its excellent approximation capability, adaptability and learning capability for uncertain systems, RBFNN is used to approximate the model of X-Y table. Aiming to improve the generalization capability of deterministic learning theory, a generalization rule of deterministic learning theory for X-Y table is proposed. Based on the proposed generalizable deterministic learning control scheme, the tracking accuracy of the X-Y table is improved and the effectiveness of the generalization rules are verified through corresponding experiments.
KW - Deterministic learning
KW - PMSM
KW - RBFNN
KW - X-Y table
KW - trajectory tracking control
UR - https://www.scopus.com/pages/publications/85146325834
U2 - 10.1109/ICIT48603.2022.10002723
DO - 10.1109/ICIT48603.2022.10002723
M3 - 会议稿件
AN - SCOPUS:85146325834
T3 - Proceedings of the IEEE International Conference on Industrial Technology
BT - 2022 IEEE International Conference on Industrial Technology, ICIT 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Industrial Technology, ICIT 2022
Y2 - 22 August 2022 through 25 August 2022
ER -