Backstepping control of MEMS gyroscope using adaptive neural observer

In this paper, a backstepping controller with an adaptive neural states observer is proposed for MEMS (Micro-Electro-Merchanical-System) gyroscopes in the presence of model uncertainties and external disturbance. Gyroscope states are usually assumed to be available in controller design procedure. However, gyroscope states may be unavailable in some circumstances. In this paper, an adaptive neural states observer is employed to estimate gyroscope states without physical sensors and thus can help reducing complexity of the gyroscope system. A backstepping controller is utilized to control the vibrating amplitude and frequency of the mass proof and the control law is carried out with states estimation rather than actual gyroscope states. Adaptive laws are investigated in the Lyapunov stability framework to guarantee the stability of the observer. Numerical simulation results demonstrate the effectiveness of the proposed control scheme.