TY - GEN
T1 - A Novel Terminal Sliding Mode Control Based on RBF Neural Network for the Permanent Magnet Synchronous Motor
AU - Ge, Yang
AU - Yang, Lihui
AU - Ma, Xikui
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/23
Y1 - 2018/8/23
N2 - This paper presents a novel terminal sliding mode control (TSMC) based on the radial basis functions neural network (RBFNN) for the permanent magnet synchronous motor (PMSM). The designed controller is composed of a RBFNN and a terminal sliding mode controller. The RBFNN is introduced to approximate the uncertainties of the PMSM system. And a novel adaptive algorithm is proposed to achieve the finite time convergence of the connection weights of RBFNN to the ideal value, which improves the system control performance and reduces the chattering. Combined with the RBFNN, a terminal sliding mode controller is designed for the PMSM speed tracking. The stability of the closed loop system is proved according to Lyapunov stability theory. The effectiveness of the proposed method is verified by the corresponding simulations, and the results show that the proposed controller possesses the better speed tracking performance.
AB - This paper presents a novel terminal sliding mode control (TSMC) based on the radial basis functions neural network (RBFNN) for the permanent magnet synchronous motor (PMSM). The designed controller is composed of a RBFNN and a terminal sliding mode controller. The RBFNN is introduced to approximate the uncertainties of the PMSM system. And a novel adaptive algorithm is proposed to achieve the finite time convergence of the connection weights of RBFNN to the ideal value, which improves the system control performance and reduces the chattering. Combined with the RBFNN, a terminal sliding mode controller is designed for the PMSM speed tracking. The stability of the closed loop system is proved according to Lyapunov stability theory. The effectiveness of the proposed method is verified by the corresponding simulations, and the results show that the proposed controller possesses the better speed tracking performance.
KW - Adaptive control
KW - Neural network
KW - Permanent magnet synchronous motor
KW - Terminal sliding mode control
KW - Uncertainty estimation
UR - https://www.scopus.com/pages/publications/85053838528
U2 - 10.1109/SPEEDAM.2018.8445201
DO - 10.1109/SPEEDAM.2018.8445201
M3 - 会议稿件
AN - SCOPUS:85053838528
SN - 9781538649411
T3 - SPEEDAM 2018 - Proceedings: International Symposium on Power Electronics, Electrical Drives, Automation and Motion
SP - 1227
EP - 1232
BT - SPEEDAM 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, SPEEDAM 2018
Y2 - 20 June 2018 through 22 June 2018
ER -