Adaptive backstepping control of MEMS gyroscope based on neural state observer

In this paper, an adaptive backstepping controller with state observer is proposed for an MEMS gyroscope in the presence of model uncertainties and external disturbances. A neural state observer is employed to estimate the MEMS gyroscope states incorporated in the backstepping controller. The neural network is utilized to approximate the nonlinear part of the gyroscope; the adaptive laws are investigated in the Lyapunov stability framework to grantee the accuracy of the observer. The backstepping controller is utilized to control the vibrating amplitude and frequency of the mass proof, and the control law is carried out with the observed states of the gyroscope. The stability of the closed-loop system can be guaranteed with the proposed observer based backstepping control. Numerical simulation results demonstrate the effectiveness of the proposed adaptive observer scheme.