Adaptive Speed Regulation for Permanent Magnet Synchronous Motor Systems With Speed and Current Constraints

This brief studies the speed regulation of permanent magnet synchronous motor systems with unknown parameters and disturbance under speed and current constraints. The neural network is adopted to approximate the uncertain system functions resulted from the unknown parameters and disturbance. In order to ensure the effectiveness of the approximation, the state variables of the approximated functions must remain within a compact set. However, in existing results, the proof of the existence of the compact set is often neglected. In this brief, an adaptive neural controller is designed based on the backstepping method. To handle the feasibility condition problem in the existing backstepping approach, the system transformation method is used, and a feasible virtual control signal is designed to replace the traditional virtual control law in the backstepping process. Furthermore, the existence of the compact set is proved. It is shown that the speed can track the pre-given reference speed, and the current and speed constraints are not violated. Simulation results demonstrate the effectiveness of the proposed controller.