Adaptive iterative learning control for MEMS gyroscope

This paper proposes a framework, namely adaptive iterative learning control (AILC) which is used in the control of Microelectromechanical systems (MEMS) gyroscopes, to realize the high-precision trajectory tracking control. According to the characteristics of MEMS gyroscope´s model, the proposed AILC algorithm includes adaptive law of parametric estimation and 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 Lyapunov-like approach, which shows that the designed controller can guarantee the globally asymptotically stability of the systems and make the output tracking error converge to zero completely when the iteration index tends to infinite. By comparing AILC and traditional PD-ILC, the simulation results demonstrate the effectiveness of AILC, and its robustness against external random disturbance.