TY - GEN
T1 - Repetitive Control of Two-mass Systems based on the Singular Perturbation Technique and Periodical Disturbance Observer
AU - Zheng, Dong Dong
AU - Li, Weixing
AU - Ren, Xuemei
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/5/14
Y1 - 2021/5/14
N2 - In this paper, a repetitive learning control strategy for two-mass systems is proposed based on the singular perturbation technique (SPT) and a periodical disturbance observer (PDOB). Firstly, the original higher order system is decomposed into two lower order subsystems using the SPT. Then PDOBs are designed for each subsystem, and sliding mode controllers are designed using the estimation results to achieve exponential convergence. Compared to other methods where the controller is designed based on the high order system model, the developed control method combines the SPT and PDOB together, thus the controller design problem is simplified and the control accuracy is improved because the periodic nature of the system is exploited. Furthermore, to reduce the oscillation at the beginning of each period when the PDOB is employed, a new repetitive learning law is developed, where an additional parameter is introduced to adjust the damping effect during the disturbance observation. The stability of the PDOB and the closed-loop system is analyzed via the Lyapunov approach and the effectiveness of the proposed PDOB and controller is verified by simulations.
AB - In this paper, a repetitive learning control strategy for two-mass systems is proposed based on the singular perturbation technique (SPT) and a periodical disturbance observer (PDOB). Firstly, the original higher order system is decomposed into two lower order subsystems using the SPT. Then PDOBs are designed for each subsystem, and sliding mode controllers are designed using the estimation results to achieve exponential convergence. Compared to other methods where the controller is designed based on the high order system model, the developed control method combines the SPT and PDOB together, thus the controller design problem is simplified and the control accuracy is improved because the periodic nature of the system is exploited. Furthermore, to reduce the oscillation at the beginning of each period when the PDOB is employed, a new repetitive learning law is developed, where an additional parameter is introduced to adjust the damping effect during the disturbance observation. The stability of the PDOB and the closed-loop system is analyzed via the Lyapunov approach and the effectiveness of the proposed PDOB and controller is verified by simulations.
KW - Periodical disturbance observer
KW - Repetitive learning control
KW - Singular perturbation
KW - Two-mass system
UR - http://www.scopus.com/inward/record.url?scp=85114200054&partnerID=8YFLogxK
U2 - 10.1109/DDCLS52934.2021.9455532
DO - 10.1109/DDCLS52934.2021.9455532
M3 - Conference contribution
AN - SCOPUS:85114200054
T3 - Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021
SP - 1070
EP - 1075
BT - Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021
A2 - Sun, Mingxuan
A2 - Zhang, Huaguang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021
Y2 - 14 May 2021 through 16 May 2021
ER -