Affiliations: [a] Electrical Engineering College, Henan University of Science and Technology, Luoyang 471023, Henan, China | [b] Information Engineering College, Henan University of Science and Technology, Luoyang 471023, Henan, China | [c] Luoyang Mining Machinery Engineering Design Institute, Luoyang 471039, Henan, China
Correspondence:
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Corresponding author: Wenshao Bu, Electrical Engineering College, Henan University of Science and Technology, Kaiyuan Road No. 263, Luolong District, Luoyang 471023, Henan, China. E-mail: [email protected].
Abstract: To achieve the radial displacement self-sensing detection of a bearingless induction motor, a prediction model or estimation method of radial displacement based on least squares support vector machine (LS-SVM) is presented. Firstly, the nonlinear relationship between the radial displacement of bearingless rotor and the currents of two sets of stator windings is analyzed. Then, the stator current components of torque windings and suspension windings, and the rotor flux-linkage of torque system are taken as input variables, the acceleration of α and β radial displacement components are taken as output variables, and based on the regression principle of LS-SVM, the predication model or estimation method of radial displacement based on LS-SVM is designed. Based on this, the radial displacement sensorless control system of a bearingless induction motor is constructed. Simulation results have shown that when the presented displacement estimation method is adopted, quicker tracking speed and higher tracking accuracy of radial displacement can be obtained; meanwhile the control system of bearingless induction motor has stronger ability of anti load torque disturbance.
Keywords: Bearingless induction motor, displacement prediction model, least squares support vector machine, control system, simulation analysis
