Nonlinear flux linkage modeling of a bearingless permanent magnet synchronous motor based on AW-LSSVM regression algorithm

Abstract

Considering the nonlinear magnetization characteristic of bearingless permanent magnet synchronous motors (BPMSMs), this paper describes a novel nonlinear model of the flux linkage for BPMSMs using adaptive weighted (AW) least square support vector machine (AW-LSSVM) regression algorithm. The inputs of the AW-LSSVM are the rotor angle, and torque winding current and suspension force winding current, and the output of the AW-LSSVM is the flux linkage of the BPMSM. Firstly, the LSSVM regression algorithm is used to build the model according to the sample data and obtain the fitting errors of the sample data. Thus, the initial error weights can be calculated on the basis of the fitting errors. And then, the lever weights can be defined based on the space distribution of the sample data. Secondly, according to the error weights and lever weights, the sample weights can be obtained adaptively by the proposed weight value iterative scheme. Finally, the relation between inputs and output is trained and the accuracy AW-LSSVM model of flux linkage can be gained. To compare the property of the proposed flux linkage model, the AW-LSSVM and the LSSVM are employed to build the flux linkage model. The simulation results indicate that by using the proposed AW-LSSVM regression algorithm, the influence of the unavoidable outliers on the model property can be effectively eliminated and the superior performance in high precision, strong robustness and quick convergence can also be obtained.