TY - GEN
T1 - Fractional-Order Nonsingular Terminal Sliding Mode Control of Uncertain Robot Neural Network
AU - Zhang, Weihai
AU - Guo, Jianguo
AU - Yu, Zunjie
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/8
Y1 - 2020/8
N2 - Aiming at the problem of low tracking accuracy and slow convergence speed of robot trajectory tracking control system with uncertainties and external disturbances, an adaptive fractional-order fast terminal sliding mode controller based on radial basis function (RBF) neural network is proposed. First of all, the method adopts nonsingular fast terminal sliding mode control, which makes the system converge to the equilibrium point in a limited time, and uses the fractional-order controller to improve the tracking performance of the controller. Moreover, we use RBF neural network to approximate unknown non-linear function of the system, and combine with adaptive compensation mechanism to realize model-free control. The stability of the closed-loop system is proved by the Lyapunov stability theorem. Finally, taking the manipulator as an example to verify the theory. The simulation results show that the proposed method can improve the tracking performance and system convergence speed, enhance the robustness to modeling errors and external disturbances, and weaken the chattering generated by the system.
AB - Aiming at the problem of low tracking accuracy and slow convergence speed of robot trajectory tracking control system with uncertainties and external disturbances, an adaptive fractional-order fast terminal sliding mode controller based on radial basis function (RBF) neural network is proposed. First of all, the method adopts nonsingular fast terminal sliding mode control, which makes the system converge to the equilibrium point in a limited time, and uses the fractional-order controller to improve the tracking performance of the controller. Moreover, we use RBF neural network to approximate unknown non-linear function of the system, and combine with adaptive compensation mechanism to realize model-free control. The stability of the closed-loop system is proved by the Lyapunov stability theorem. Finally, taking the manipulator as an example to verify the theory. The simulation results show that the proposed method can improve the tracking performance and system convergence speed, enhance the robustness to modeling errors and external disturbances, and weaken the chattering generated by the system.
KW - Fractional-Order Sliding Mode Control
KW - RBF Neural Network
KW - Trajectory Tracking
KW - Uncertain Robot
UR - http://www.scopus.com/inward/record.url?scp=85091599533&partnerID=8YFLogxK
U2 - 10.1109/CCDC49329.2020.9164459
DO - 10.1109/CCDC49329.2020.9164459
M3 - 会议稿件
AN - SCOPUS:85091599533
T3 - Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020
SP - 4584
EP - 4589
BT - Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 32nd Chinese Control and Decision Conference, CCDC 2020
Y2 - 22 August 2020 through 24 August 2020
ER -