Magnetic Field Compensation Control for Spin-Exchange Relaxation-Free Comagnetometer Using Reinforcement Learning

Abstract

The tri-axial drift magnetic field compensation (TDMFC) is a prerequisite to maintaining excellent performance of the Spin-Exchange Relaxation-Free Comagnetometer (SER-FCM). In this article, we develop a previously undescribed controller design architecture running the proposed constrained dynamic action space Q-learning (CDA-Q) algorithm using reinforcement learning (RL) to solve the TDMFC problem. The architecture contains two parts: offline training and online deployment. Specifically, the CDA-Q algorithm trains the agents with the simulated environment to produce the control strategies adopted in the online deployment. Numerical simulations verify the effectiveness of the obtained control strategies. Experimentally, the control strategies are deployed in the real-time control system achieving efficient and adaptive compensation of the tri-axial drift magnetic field. Comparative experiments show that the proposed method is 67.56% more efficient than the existing method.