Closed-Loop Control of Risley Prism Based on Deep Reinforcement Learning

Yuxiang, Yao; Chen, Ke; Li, Jinying; Congming, Qin

doi:10.1109/iccea50009.2020.00109

2020 International Conference on Computer Engineering and Application (ICCEA) 2020

DOI: 10.1109/iccea50009.2020.00109

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Closed-Loop Control of Risley Prism Based on Deep Reinforcement Learning

Yao Yuxiang

Ke Chen

Jinying Li

et al.

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2022

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Robust Risley prism control based on disturbance observer for system line-of-sight stabilization

Wang

et al. 2022

Appl. Opt.

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In this paper, a robust control based on disturbance observer is proposed to improve the tracking accuracy of the Risley prism system (RPS). Applying the flexible thin-wall ring mechanism in the RPS causes a series of tracking and pointing challenges. Disturbances such as friction, shaft deformation, and model perturbation significantly deteriorate the tracking and pointing accuracy of the RPS. Two different observer-based control methods are proposed to guarantee the tracking precision of the RPS. Moreover, the disturbance observation and compensation (DOC) performance of the proposed methods is analyzed and compared. Finally, simulation and experiment results indicate that the proposed control methods, especially the DOC-expanded state observer control mode, obtain the best performance for disturbance rejection in the RPS.

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Robust Risley prism control based on disturbance observer for system line-of-sight stabilization

Wang

et al. 2022

Appl. Opt.

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Closed-Loop Control of Risley Prism Based on Deep Reinforcement Learning

Cited by 1 publication

References 5 publications

Robust Risley prism control based on disturbance observer for system line-of-sight stabilization

Robust Risley prism control based on disturbance observer for system line-of-sight stabilization

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