Optimal Dual-Rate Digital Redesign with Application to Missile Control
Bibliographic record
Abstract
8Song, T. L., Shin, J. S., and Han, H. S., “Impact Angle Control for Planar Engagements,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 35, No. 4, 1999, pp. 1439–1444. 9Song, T. L., and Shin, J. S., “Time Optimal Impact Angle Control for Vertical Plane Engagements,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 35, No. 2, 1999, pp. 738–742. 10Savkin, A. V., Pathirana, P., and Faruqi, F. A., “The Problem of Precision Missile Guidance: LQR and H∞ Frameworks,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 39, No. 3, 2003, pp. 901–910. 11Petersen, I. R., and Savkin, A. V., Robust Kalman Filtering for Signals and Systems with Large Uncertainties, Birkhauser, Boston, MA, 1999. 12Savkin, A. V., and Petersen, I. R., “Recursive State Estimation for Uncertain Systems with an Integral Quadratic Constraint,” IEEE Transactions on Automatic Control, Vol. 40, No. 6, 1995, pp. 1080–1083. 13Petersen, I. R., Ugrinovskii, V. A., and Savkin, A. V., Robust Control Design Using H∞ Methods, Springer-Verlag, London, 2000, Chap. 5. 14Savkin, A. V., and Petersen, I. R., “Nonlinear Versus Linear Control in the Absolute Stabilizability of Uncertain Linear Systems with Structured Uncertainty,” IEEE Transactions on Automatic Control, Vol. 40, No. 1, 1995, pp. 122–127. 15Singer, R. A., “Estimating Optimal Tracking Filter Performance for Manned Maneuvering Targets,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 6, No. 4, 1970, pp. 473–483.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".