Adaptive backstepping global sliding fuzzy neural controller for MEMS gyroscope

Abstract

An adaptive backstepping global proportional integral derivative (PID) sliding mode fuzzy control with a radial basis function (RBF) neural network (NN) estimator is proposed for a micro electromechanical systems (MEMS) gyroscope. First of all, a backstepping sliding mode controller is designed to compensate the external disturbances and a RBF neural network controller is employed to approach the system unknown dynamic characteristic. Additionally, sliding mode term in the backstepping global PID sliding mode controller is adjusted by the adaptive fuzzy system to compensate for the NN approximation error and the external disturbances, reducing the chattering phenomenon. Finally, simulation results demonstrate the effectiveness of the backstepping global sliding fuzzy controller with RBF neural approximator.