TY - GEN
T1 - Adaptive Neural Learning Prescribed-Time Control for Teleoperation Systems With Output Constraints
AU - Li, Longnan
AU - Liu, Zhengxiong
AU - Guo, Shaofan
AU - Ma, Zhiqiang
AU - Huang, Panfeng
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this paper, the control performance of the teleoperation system subjected to dynamics uncertainty and external disturbance is investigated. To improve control performance, an adaptive neural learning prescribed-time controller was developed, which ensures that the system's output tracks the desired trajectory with a predetermined accuracy within a user-defined time. Unlike other general finite-time or fixed-time controllers, the predetermined convergence time can be exactly obtained rather than approximated. Moreover, the proposed control scheme can solve the issue with and without constraints uniformly. With the aid of the Lyapunov method, the stability of the system is analyzed. Finally, the effectiveness of the proposed method is further verified by numerical simulations.
AB - In this paper, the control performance of the teleoperation system subjected to dynamics uncertainty and external disturbance is investigated. To improve control performance, an adaptive neural learning prescribed-time controller was developed, which ensures that the system's output tracks the desired trajectory with a predetermined accuracy within a user-defined time. Unlike other general finite-time or fixed-time controllers, the predetermined convergence time can be exactly obtained rather than approximated. Moreover, the proposed control scheme can solve the issue with and without constraints uniformly. With the aid of the Lyapunov method, the stability of the system is analyzed. Finally, the effectiveness of the proposed method is further verified by numerical simulations.
KW - neural network
KW - output constraints
KW - prescribed-time control
KW - teleoperation
UR - http://www.scopus.com/inward/record.url?scp=85143904778&partnerID=8YFLogxK
U2 - 10.1109/IECON49645.2022.9969103
DO - 10.1109/IECON49645.2022.9969103
M3 - 会议稿件
AN - SCOPUS:85143904778
T3 - IECON Proceedings (Industrial Electronics Conference)
BT - IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society
PB - IEEE Computer Society
T2 - 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022
Y2 - 17 October 2022 through 20 October 2022
ER -