Evaluation of Precise Microwave Ranging Technology for Low Earth Orbit Formation Missions with Beidou Time-Synchronize Receiver

Wang, Xiaoliang; Wu, Shufan; Gong, Dunwei; Shen, Qiang; Wang, Dengfeng; Damaren, Christopher J.

doi:10.3390/s21144883

Cited by 4 publications

(1 citation statement)

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“…Qiu [48] provided an optimal operation solution of chillers by combining RL technique and expertise knowledge, aiming for a balance of power and indoor comfort. Inspired by the literature, this paper focuses on a trigger-based MWR payload thermal control system design with an online learning iteration, aiming to the micron-level precise ranging performance in space missions [49,50]. The proposed approach benefits from the following noteworthy features:…”

Section: Introductionmentioning

confidence: 99%

Trigger-Based K-Band Microwave Ranging System Thermal Control with Model-Free Learning Process

et al. 2022

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Micron-level accuracy K-band microwave ranging in space relies on the stability of the payload thermal control on-board; however, large quantities of thermal sensors and heating devices around the deployed instruments consume the precious inner communication resources of the central computer. Another problem arises, which is that the payload thermal protection environment can deteriorate gradually through years operating. In this paper, a new trigger-based thermal system controller design is proposed, with consideration of spaceborne communication burden reduction and actuator saturation, which guarantees stable temperature fluctuations of microwave payloads in space missions. The controller combines a nominal constant sampling PID inner loop and a trigger-based outer loop structure under constraints of heating device saturation. Moreover, an iterative model-free reinforcement learning process is adopted that can approximate the estimation of thermal dynamic modeling uncertainty online. Via extensive experiment in a laboratory environment, the performance of the proposed trigger thermal control is verified, with smaller temperature fluctuations compared to the nominal control, and obvious efficiency in system communications. The online learning algorithm is also tested with deliberate thermal conditions that deviate from the original system—the results can quickly converge to normal when the thermal disturbance is removed. Finally, the ranging accuracy is tested for the whole system, and a 25% (RMS) performance improvement can be realized by using a trigger-based control strategy—about 2.2 µm, compared to the nominal control method.

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Section: Introductionmentioning

confidence: 99%