The reinforcement learning PID control method for the driving system of micro-machined gyroscope

Micro-machined gyroscope (MMG) has wide application prospects in the economic and military fields, especially in the military fields where weight and volume are important value. However, the precision of MMG are affected by the mechanical thermal noise, the Coriolis force of the driving mode coupled by the sensing mode, the quadrature error and the coupling damping. It is necessary to establish the more realistic gyroscope structural model and improve the closed loop driving control. In this paper, the structural model of MMG which can describe the mechanical thermal noise, the Coriolis force of the driving mode coupled by the sensing mode, the quadrature error and the coupling damping is proposed firstly. For the proposed gyroscope model, the closed-loop driving control based on the reinforcement learning PID algorithm is applied to improve the precision of MMG. The simulation results show that the reinforcement learning PID controller can fulfill the requirements of the rapid start-up and the overshoot reducing. The control system can stabilize the amplitude and track the resonant frequency of the driving mode. It is important for the applications of MMG.