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

Xin Su; Lixin Xu; Bingbing Fan

doi:10.1117/12.2550515

31 January 2020 The reinforcement learning PID control method for the driving system of micro-machined gyroscope

Xin Su, Lixin Xu, Bingbing Fan

Proceedings Volume 11427, Second Target Recognition and Artificial Intelligence Summit Forum; 114270R (2020) https://doi.org/10.1117/12.2550515
Event: Second Target Recognition and Artificial Intelligence Summit Forum, 2019, Changchun, China

Abstract

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.

Citation Download Citation

Xin Su, Lixin Xu, and Bingbing Fan "The reinforcement learning PID control method for the driving system of micro-machined gyroscope", Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114270R (31 January 2020); https://doi.org/10.1117/12.2550515

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