Neural network based torque ripple minimisation in a switched reluctance motor

This paper presents an artificial neural network (ANN) solution to torque ripple reduction in a switched reluctance motor. Magnetic saturation together with salient stator and rotor poles give rise to a highly nonlinear torque/current/angle characteristic. The approach in this paper allows the neural network to be used to its full potential, that is, learning the nonlinear flux linkage characteristic while also incorporating a priori analytical knowledge of the torque production mechanism of the machine. This combination of neuro-learning and analytical insight results in a greatly simplified controller. Simulation results are presented to illustrate the performance of the proposed technique. Experimental results based on a floating point DSP processor are included.< >