In this paper, after introducing the bearingless motor's working principle in brief, we analyze the magnetic field distribution principle of the bearingless motor. We then derive in detail the general analytical models of inductance matrices of a four-pole bearingless motor with two-pole controlling windings. The derived general analytical models are applicable not only to bearingless motors with a salient-pole rotor, but also to bearingless motors with a cylindrical rotor. By setting different values of salient-pole arc width parameter rho in the general models of inductance, the analytical models of inductance of different types of bearingless motors, such as bearingless induction motors, bearingless synchronous reluctance motors, and bearingless surface-mounted permanent magnet (SPM) motors, can be derived.

BLIM, i.e. bearingless induction motor, is a multi-variable, nonlinear and strong coupling object. To achieve its dynamic decoupling control with high performance, a novel dynamic decoupling control strategy is proposed. After analyzing the working p

Bearingless induction motor is a multi variable, nonlinear and strong coupling object, in order to achieve its suspension control with high performance, the dynamic model of magnetic suspension system is established; and the radial displacement of bearingless rotor is separated into two parts, include random displacement component and unbalanced vibration displacement component. Then by inverse system method, dynamic decoupling control between the two random displacement components is achieved. On this basis, through the compensation controller of unbalanced vibration, the compensation force of unbalanced vibration is regulated and estimated real time, and the control current of unbalanced vibration compensation force is calculated. The compensation force is used to counteract the unbalanced exciting force of bearingless rotor. Under the dq coordinate system oriented by the rotor flux-linkage of torque system, the control current compensation of unbalanced vibration is achieved. At the end, simulation verification and analysis are made. From the simulation results, it is clear that when the proposed control strategy is adopted, the unbalanced vibration of bearingless rotor can be inhibited effectively in the start stage; the unbalanced vibration displacement can be eliminated in steady state. The proposed control strategy is effective and feasible.

Aiming at the unbalanced vibration problem of bearingless induction motor caused by the rotor mass eccentricity, an adaptive LMS extraction method of unbalanced displacement is proposed. Firstly, the characteristics of unbalanced displacement signal, the influences of step size factor on the tracking speed and the steady state tracking error are analyzed; then in order to solve the contradiction between tracking speed and steady state tracking error, and achieve the quick extraction of unbalanced displacement, a convenient nonlinear function of step size factor is adopted. On this basis, an unbalanced vibration control system of a bearingless induction motor is constructed; simulation verification and analysis are made. The simulation results have shown that the unbalanced displacement signal can be extracted quickly in dynamic status, a higher extraction precision of unbalanced displacement can be obtained in steady status; based the proposed extraction method of unbalanced displacement signal, the magnetic suspension control performance of a bearingless induction motor can be improved greatly.

The bearingless induction motor (BLIM) is a multi-variable, non-linear, strong coupling system. To achieve higher performance control, a novel neural network inverse system decoupling control strategy considering stator current dynamics is proposed. Taking the stator current dynamics of the torque windings into account, the state equations of the BLIM system is established first. Then, the inverse system model of the BLIM is identified by a three-layer neural network; by means of the neural network inverse system method, the BLIM system is decoupled into four independent second-order linear subsystems, include a rotor flux subsystem, a motor speed subsystem and two radial displacement component subsystems. On this basis, the neural network inverse decoupling control system is constructed, the simulation verification and analyses are performed. From the simulation results, it is clear that when the proposed decoupling control strategy is adopted, not only can the dynamic decoupling control between relevant variables be achieved, but the control system has a stronger anti-load disturbance ability, smaller overshoot and better tracking performance.