Dual-segment variable gain sliding mode control based on neural network for permanent magnet synchronous motor servo drives

Aimed at the problem of high-precision position control system in modern industrial control, a novel dual-segment variable gain sliding mode controller based on permanent magnet synchronous motor (PMSM) is designed. Different from the traditional control system of three closed-loop vector, the dual-segment variable gain sliding mode controller adopts the integrated design of velocity segment and position segment to carry out time sharing control according to the different operating states of the system. The variable gain global sliding mode controller is adopted in the velocity segment, and the integral sliding mode controller with variable gain is used in the position segment. The control law of the sliding surface of the velocity and position segments is composed of the equivalent control law and switching control law. In order to further improve the robustness of the system, the RBF neural network is introduced to get the switching control law. The simulation results show that the controller has good speed response performance and anti-interference ability.