Research on the least squares support vector machine displacement observer of a bearingless induction motor

Abstract

To achieve the radial displacement self-sensing detection of a bearingless induction motor, an observation method based on the LS-SVM (least squares support vector machine) is proposed. The state-space model of a magnetic suspension system is derived firstly. Then the LS-SVM is introduced to the radial displacement observer of the bearingless induction motor, the design principle and Lyapunov stability of the LS-SVM displacement observer are analysed in detail, and the construction method of the LS-SVM displacement observer is presented also. Simulation and verification results show that both in the starting suspension stage and in the process of stable suspension operation, the LS-SVM displacement observer can quickly track the radial displacement with high accuracy. Then, a new method is found for the radial displacement self-sensing detection of the bearingless induction motor.