Robust adaptive neural network control for switched reluctance motor drives

This article presents a robust adaptive neural network controller for switched reluctance motor (SRM) speed control with both parameter variations and external load disturbances. The radial basis function neural network with the technology of minimal learning parameters is employed to approximate an ideal control law which includes the parameter variations and external disturbances. Furthermore, a proportional control term is introduced to improve the transient performance and chattering phenomena of the SRM drive system. The asymptotic stability of the proposed controller is guaranteed through rigorous Lyapunov analysis. A main advantage of the proposed control scheme is that it contains only one adaptive parameter that needs to be updated on-line. This advantage result in a much simpler adaptive control algorithm, which is convenient to implement in switched reluctance drives. Finally, the simulations and experiments are carried out to demonstrate the effectiveness of the proposed control scheme.

Abstract licence: CC BY-NC

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