Adaptive neural compensation scheme for robust tracking of MEMS gyroscope

In this paper, an adaptive control strategy using radial basis function (RBF) network compensator is presented for robust tracking of MEMS gyroscope in the presence of model uncertainties and external disturbances. An adaptive neural network controller is employed to compensate such system nonlinearities and improve the tracking performance. An RBF neural network controller which can be trained on line is incorporated into the adaptive control scheme in the Lyapunov framework to guarantee the stability of the closed loop system. Numerical simulation for a MEMS angular velocity sensor is investigated to verify the effectiveness of the proposed adaptive neural control scheme and demonstrate the satisfactory tracking performance and robustness against model uncertainties and external disturbances.