Trajectory correction for lunar flyby transfers to libration point orbits using continuous thrust
Bibliographic record
Abstract
Abstract Trajectory corrections for lunar flyby transfers to Sun–Earth/Moon libration point orbits (LPOs) with continuous thrusts are investigated using an ephemeris model. The lunar flyby transfer has special geometrical and dynamical structures; therefore, its trajectory correction strategy is considerably different from that of previous studies and should be specifically designed. In this paper, we first propose a control strategy based on the backstepping technique with a dead-band scheme using an ephemeris model. The initial error caused by the launch time error is considered. Since the perturbed transfers significantly diverge from the reference transfers after the spacecraft passes by the Moon, we adopt two sets of control parameters in two portions before and after the lunar flyby, respectively. Subsequently, practical constraints owing to the navigation and propellant systems are introduced in the dynamical model of the trajectory correction. Using a prograde type 2 orbit as an example, numerical simulations show that our control strategy can efficiently address trajectory corrections for lunar flyby transfers with different practical constraints. In addition, we analyze the effects of the navigation intervals and dead-band scheme on trajectory corrections. Finally, trajectory corrections for different lunar flyby transfers are depicted and compared.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".