Papers by Keyword: Bearingless Synchronous Reluctance Motor (BSynRM)

Abstract: A novel decoupling control method based on neural networks inverse system is presented in this paper for a bearingless synchronous reluctance motor (BSRM) possessing the characteristics of multi-input-multi-output, nonlinearity, and strong coupling. The dynamic mathematical models are built, which are verified to be invertible. A controller based on neural network inverse is designed, which decouples the original nonlinear system to two linear position subsystems and an angular velocity subsystem. Furthermore, the linear control theory is applied to closed-loop synthesis to meet the desired performance. Simulation and experiment results show that the presented neural networks inverse control strategy can realize the dynamic decoupling of BSRM, and that the control system has fine dynamic and static performance.

Abstract: The bearingless synchronous reluctance motor (BSynRM) is a multi-variable, nonlinear and strong-coupled system. To solve the difficult problem of precise decoupling in electric torque and radial suspension force control, the theory of direct torque control of traditional synchronous reluctance motor are applied to the torque control of a BSynRM in this paper. Based on mathematical models of the BSynRM, A direct torque control algorithm based on space vector pulse width modulation (SVM-DTC) is deduced. The SVM-DTC control system is designed and simulated. The simulation results show the control algorithm of SVM-DTC realizes decoupling in electric torque and radial suspension force control. The static and dynamic performance of electric torque, speed and radial suspension force of the BSynRM is excellent.