Adaptive fully tuned RBF neural control of MEMS gyroscope

Abstract

In this paper, a novel adaptive control scheme that incorporates fully tuned radial basis function (RBF) neural network (NN) is proposed for the control of MEMS gyroscope with respect to external disturbances and model uncertainties. An adaptive fully tuned RBF neural network controller is used to compensate the external disturbances and model uncertainties, thus improving the dynamic characteristics and robustness of the MEMS gyroscope. The fully tuned RBF neural network compensating controller and the adaptive nominal controller are combined in the unified Lynapunov framework to ensure the stability of the control system. By using the proposed scheme, not only the effect of model uncertainties and external disturbances can be eliminated, but also satisfactory dynamic characteristics and strong robustness can be obtained. Simulation studies are implemented to verify the effectiveness of the proposed scheme and demonstrate that the fully tuned RBF network control has better robustness and dynamic characteristics than traditional RBF network control.