The 16/6/8 double-stator bearingless switched reluctance motor (DSBSRMTh is used as the object of study in this paper. To solve the problem of torque and levitation force ripples in this motor, a control system direct force control (DFCTh and direct instantaneous torque control (DITCTh based on the torque sharing function (TSFTh are proposed. With the strong nonlinearity and approximation capability of radial basis function neural networks, the torque and levitation force observer are designed. The observed torque and levitation forces are used as feedback for the internal loop control, which is combined with the external loop control to make a double closed-loop control. In order to further improve the output torque and system robustness and suppress the torque ripple in steady-state process, the motor winding method is optimized and a set of switching angles is added on the basis of TSF. The simulation results verify the effectiveness and superiority of the proposed control method. It effectively suppresses speed ripple and reduces torque and levitation force fluctuations and rotor radial displacement jitter.
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