Abstract

This paper presents a robust adaptive sliding mode control strategy of MEMS triaixal gyroscope using radial basis function (RBF) neural network. A key property of this scheme is that the prior knowledge of the upper bound of the system uncertainties is not required. Adaptive RBF neural network that could learn the unknown upper bound of model uncertainties and external disturbances is incorporated into the adaptive sliding mode control scheme in the same Lyapunov framework. The proposed adaptive sliding mode controller can update the estimates of all stiffness errors, damping terms and angular velocities in real time and guarantee the stability of the closed loop system. Numerical simulation for a MEMS triaxial angular velocity sensor is investigated to verify the effectiveness of the proposed adaptive RBF sliding mode control scheme.

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