volume | PIER Journals

Abstract

In order to solve the problems of susceptibility to environmental disturbances, complex installation and low reliability caused by the photoelectric code disks in the permanent magnet assisted bearingless synchronous reluctance motor (PMa-BSynRM) system, an improved BP neural network (BPNN) left-inverse system is proposed to establish the speed self-sensing system of the PMa-BSynRM. Firstly, the mathematical model of the PMa-BSynRM is established, and the left reversibility of the PMa-BSynRM speed subsystem is analyzed. Secondly, the traditional BP neural network is optimized from four aspects, including the number of neurons in the hidden layer, initial weight, connection weight and learning rate. Then, the improved BPNN model is used as the inverse model of the left inverse system to fit the speed subsystem of the PMa-BSynRM. Finally, the simulation results show that accurate estimation of the speed and rotor position is achieved by the proposed speed self-detection system, and the accuracy and feasibility of the proposed speed self-detection system are validated by the experimental results.

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Mr. Chao Chen

Prof. Huangqiu Zhu