Rotor Position Estimation Of Switched Reluctance Motor Based On Neural Network

Due to its simple construction, reliability, high efficiency and low cost, switched reluctance motor (SRM) has shown huge competitive power in many fields. But mechanical position sensors add to the cost, complexity and potential unreliability at high speed and this has motivated the investigation of sensorless position estimation. Because of its high nonlinear electromagnetism characteristic, the sensorless control based on accurate model of SRM is hard to be accomplished. In recent years, artificial neural network(ANN) technology has made a great progress, which gives a new method to accomplish position sensorless control of SRM.A novel indirect detecting method of SRM's rotor postion based on BP(Back Propagation) neural network is presented in this paper. For the adopted network,the training data set is comprised of magnetization data of the SRM for which three-phase current and flux linkage are inputs and the corresponding position is the output, the BP neural network can build up a correlation among phase current, phase flux linkage and position, thereby facilitating elimination of the rotor position sensor.At the present stage, in order to ensure the forecast precision and the convergence rate of the neural network, the neural network's training sample data is generally the electrical motor's operation data under the specified condition which can not reflect the real running situation of the electrical motor. In view of this situation, this paper proposes a method based on training data real-time renewal, and introduces the idea to the input vector of neural network. In neural network's training process, according to the real running situation of the electrical motor, takes the three-phase current, flux linkage and rotor position in different operation circumstances as training sample, to train the established neural network model in batches, until the network's forecast precision tends to be stable and meet the requirement.The simulation results show that the improved BP neural networks proposed in the paper can realize SRM rotor position's accurate prediction in case of different operation of the electrical motor,and realizing the motor's control without position sensor. The neural network has high forecast precision, favorable dynamic characteristics, better adaptability and robustness.