Technique for Magnetic Bearing Based on Mixed-Kernel Support Vector Machine Forecasting Model

Abstract

The self-sensing magnetic bearing can reduce the cost and the axial size of the magnetic bearing and increase its reliability. A mixed-kernel least squares support vector machines (LS-SVM) forecasting model is proposed for self-sensing technique of a hybrid magnetic bearing. The structure and mathematical model of the radial-axial hybrid magnetic bearing are introduced. Based on the principle of the mixed-kernel LS-SVM, the nonlinear forecasting model between the current and the displacement which realizes the displacement self-sensing control is built through genetic algorithm. Simulation has done to verify the validity and feasibility of proposed method.