Neural Adaptive Sliding Model Variable Structure Control for Brushless DC motor Speed Systems

In this paper a novel neural adaptive sliding model variable structure control strategy is proposed to improve the performances of brushless DC speed systems. Firstly,a sliding model variable structure speed controller of BLDCM is designed according to its mathematical model,in which an adaptive algorithm for regulating the switching gain is adopted to restrain the chattering around the sliding plane. And then radial basis function neural network (RBFNN) is devised to estimate the generalized disturbance item of the control variable dynamically. Finally,some simulation and experimental results are provide to indicate that the speed system of BLDCM by using the proposed control method has less overshoot,quick velocity response,higher control precision and good robustness,which is insensitive to the parameter chattering and many disturbances.