A hybrid double stator bearingless switched reluctance motor with advantages of high suspension output, small loss and weak coupling is presented. This motor can realise electric/power generation and radial two-degree-of-freedom suspension. The biased magnetic field for producing radial force is provided by a permanent magnet, which reduces system power consumption. Self-decoupling is realised between a torque system and suspension system according to the mechanical structure design. Furthermore, the radial force is approximately constant when the suspension current is constant, thereby improving the controllability of the system. The operation principle is introduced and the electromagnetic characteristics are analysed through the timestepping finite element method. This analysis verifies the effectiveness of the theoretical analysis. Introduction: The structure of magnetic bearings (MBs) is similar to that of the switched reluctance motor (SRM) stator; thus, the bearingless technology is applied to SRM to maximise its high-speed performance [1]. The bearingless SRM (BSRM) which combines the merits of SRM and MB can rotate and levitate simultaneously by integrating the magnetic levitation winding into the stator of motor. Several types of BSRMs have been proposed. On the design front, 12/8 BSRM was proposed by Takemoto et al. [1], 8/6 single winding BSRM was proposed by Chen and Hofmann [2], 12/8 single winding BSRM was proposed by Liu and Yang in [3]. Moreover, BSRM with special structures [4, 5] (double-stator, hybrid-stator and hybrid-rotor) was proposed. In this Letter, the idea of 'hybrid excitation' is introduced into double stator BSRM (DSBSRM), and a new hybrid DSBSRM (HDSBSRM) is presented. The structure, operation principle and main electromagnetic characteristics of the HDSBSRM are analysed.

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Abstract:The motors' flux-linkage, current and angle obtained from the system with sensors were chosen as the sample data, and the estimation model of rotor position based on relevance vector machine (RVM) was built by training the sample data. The kernel function parameter in RVM model was optimized by the particle swarm algorithm in order to increase the fitting precision and generalization ability of RVM model. It achieved higher prediction accuracy with staying at the same on-line testing time as the RVM. And because the short on-line computation, the motor can operate at 3000 r/min in sensorless control with particle swarm optimization-relevance vector machine (PSO-RVM), which is higher than support vector machine (SVM) and neural network (NN). By simulation and experiment on the test motor, it is verified that the proposed estimation model can obtain the angle of full electrical period accurately under low speed and high speed operations in current chopped control and angle position control, which has satisfactory estimation precision.