Trigger-Based K-Band Microwave Ranging System Thermal Control with Model-Free Learning Process
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".