Position Sensorless Control of SRMs Based on Novel BP Neural Network
Neural Network (NN) has been proved ideal in nonlinear fitting. It is applied as the rotor position estimator in the Switched Reluctance Motors (SRMs) whose characteristic is highly nonlinear. However, the conventional BP NN based rotor position estimator was inappropriate to be implemented in real-time application at high speed operations, because of its considerable computational time consumption in hidden layer. In this paper, a novel BP NN based estimator with pre-treatment is proposed, which considerably simplifies the original neural network structure. It achieves a 40.2％ computational burden reduction while staying at the same accuracy as the conventional one. Sensorless control algorithm is also put forward and simulated in order to testify the proposed sensorless estimator and control strategy.
Sensorless Control BP Neural Network Switched Reluctance Motor
Yu-Bo WANG Rui ZHONG Yu-Zhe XU Sheng-Li LU
National ASIC Sys. Eng. Research Center Southeast University Nanjing, China
国际会议
长春
英文
304-307
2010-08-24（万方平台首次上网日期，不代表论文的发表时间）