Study of a MEMS Vibratory Gyroscope Using Adaptive Iterative Learning Control

This paper proposes a framework, namely adaptive iterative learning control (AILC), which is used in the control of a microelectromechanical system (MEMS) gyroscope, to realize high-precision trajectory tracking control. According to the characteristics of the MEMS gyroscope's model, the proposed AILC algorithm includes an adaptive law of parametric estimation and an iteration control law, which is updated in the iterative domain without any prior knowledge of MEMS gyroscopes. The convergence of the method is proven by a Lyapunov-like approach, which shows that the designed controller can guarantee the stability of the system and make the output tracking errors to converge completely to zero while the iteration index tends to infinity. By comparing AILC and traditional PD-ILC, the simulation results demonstrate the effectiveness of AILC and its robustness against external random disturbance.