Identification of switched reluctance motor states using application specific artificial neural networks

In this paper a novel artificial neural network architecture suitable to identify the states of a switched reluctance motor is developed. This architecture incorporates the a priori knowledge about the motor directly into the structure of a feedforward artificial neural network. A method for backpropagating the error is presented with emphasis given to the specifically developed application specific layers for the switched reluctance motor. The switched reluctance motor model is given. A summary of the integration of the motor model into the ANN is presented. Simulation results show increased convergence rates as well as superior overall identification of the motor states.