Pseudospectral-Based Rapid Trajectory Planning and Feedforward Linearization Guidance
Bibliographic record
Abstract
A trajectory-based guidance strategy is proposed for the three-dimensional terminal return task of an uncrewed space vehicle (USV). The overall guidance scheme consists of reference trajectory planning and robust trajectory tracking modules. The trajectory planning algorithm involves determining the motion of the USV to achieve a prescribed target under multiple constraints. The altitude-domain-based USV model is firstly proven to be differentially flat utilizing the dynamic pressure and position of the USV as flat outputs. The original trajectory planning problem is reformulated in a lower-dimensional flat output space. The discretization of the planning problem is then achieved using the pseudospectral method, based on which an initial guess technique is designed in order to accelerate the solving speed of the planning algorithm. Subsequently, a feedforward linearization-based trajectory tracking guidance law is designed using the differential flatness property of the altitude-domain model. Simulation results in different scenarios show that the proposed guidance strategy provides a satisfactory guidance solution.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".