Adaptive position tracking control of permanent magnet synchronous motor based on RBF fast terminal sliding mode control

This paper focuses on the performance improvements of the permanent magnet synchronous motor (PMSM) using vector control. In this paper, a neural adaptive sliding mode control algorithm is proposed to accomplish the position tracking of the field-oriented control (FOC) for PMSM. The proposed algorithm is presented by combining the fast terminal sliding mode (FTSM) with the radial basis function (RBF). Hence, the algorithm can not only compensate the network approximation errors but also solve the problem that FTSM is greatly dependent on the parameters of the PMSM. Furthermore, it is conducted easily and improves the performance of the PMSM control system, such as the tracking accuracy, robustness and response speed, etc. The neural network parameters are updated according to the Lyapunov approach which is used to prove the stability of the closed-loop system. The experimental results testify that the proposed algorithm is feasible and effective and is capable of controlling the PMSM in the real applications.