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In this paper, a neural sliding mode control approach is developed to adjust the sliding gain using a radial basis function (RBF) neural network (NN) for the tracking control of Microelectromechanical Systems (MEMS) triaxial vibratory gyroscope. First a sliding mode control with a fixed sliding gain is proposed to assure the asymptotic stability of the closed loop system. Then a RBF neural network is derived to adjust the sliding gain using a gradient method in a switching control law. With the adaptive sliding gain using the learning function of RBF neural network, the chattering phenomenon is eliminated. Numerical simulation is investigated to verify the effectiveness of the proposed neural sliding mode control scheme.

Modeling and neural sliding mode control of mems triaxial gyroscope