Near-Optimal Trajectory Shaping of Guided Projectiles with Constrained Energy Consumption
Bibliographic record
Abstract
An iterative search based on cross-entropy minimization is proposed to generate a sequence of lateral acceleration signals that command a guided projectile to a prescribed target set. The proposed approach is based on iterative simulations, thus enabling the projectile to satisfy, when possible, tight terminal constraints while taking into account nonlinear flight dynamics. To do so, a trajectory shaper is proposed. The trajectory shaper is composed of a discrete-valued lateral acceleration command in series with a smoothing filter. Non-gradient-based iterative searches are utilized to quickly determine the sequence of lateral accelerations that fulfill the control objectives. Simulations show that the crossentropy method achieves near optimality in a satisfactory period of time. Furthermore, it is shown that energy expenditure can be limited while meeting the target set by simply including a carefully designed smoothing filter rather than trying to minimize a more complex objective function.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".