Rotor Displacement Self-Sensing Modeling of Six-Pole Radial Hybrid Magnetic Bearing Using Improved Particle Swarm Optimization Support Vector Machine

An inverter-fed six-pole radial hybrid magnetic bearing (HMB) has the characteristics of compact structure, high support speed, long service life, and so on. However, using displacement sensors to detect rotor displacements leads to the problems of large volume, high cost, and low reliability. In this article, a self-sensing method using improved particle swarm optimization (IPSO) least square support vector machine (LS-SVM) is proposed, which eliminates the influences of displacement sensors fundamentally. The structure and working principle of six-pole radial HMB are introduced, and the mathematical model of its radial suspension force is deduced according to the equivalent magnetic circuit method. Based on the regression principle of the LS-SVM, the prediction model between the currents in control coils and the rotor displacements is established. Also, the performance parameters of LS-SVM are optimized by the IPSO algorithm, which realizes self-sensing modeling of rotor displacement. The simulation system for self-sensing modeling of rotor displacement for six-pole radial HMB is constructed, and floating experiment, static suspension experiment, dynamic suspension experiment, and disturbance experiment of the rotor are carried out, which verify the robustness and stability of the self-sensing method proposed.