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Proceedings Paper

The reinforcement learning PID control method for the driving system of micro-machined gyroscope
Author(s): Xin Su; Lixin Xu; Bingbing Fan
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Paper 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.

Paper Details

Date Published: 31 January 2020
PDF: 6 pages
Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114270R (31 January 2020); doi: 10.1117/12.2550515
Show Author Affiliations
Xin Su, Beijing Institute of Technology (China)
Lixin Xu, Beijing Institute of Technology (China)
Bingbing Fan, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 11427:
Second Target Recognition and Artificial Intelligence Summit Forum
Tianran Wang; Tianyou Chai; Huitao Fan; Qifeng Yu, Editor(s)

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