Backstepping Guidance for Missiles Modeled as Uncertain Time-Varying First-Order Systems
Bibliographic record
Abstract
This paper proposes a Lyapunov-based guidance law, which takes into account the nonlinear relative kinematics between the missile and the target, and ensures ultimate boundedness of the missile-target system trajectories provided the estimation error of the target acceleration is bounded in magnitude. The proposed guidance synthesis, which combines high-gain backstepping and variable structure approach, takes into account the uncertain flight control dynamics. Numerical simulations of the proposed guidance in closed-loop with an interval 2nd-order missile transfer function and a maneuvering target demonstrate satisfactory performances when compared to several other modern and classical guidance laws. Furthermore, it is shown that using the estimate of the target acceleration in the guidance allows achieving a relatively small miss distance when the pursuer-evader maximum maneuverability ratio approaches unity. However, the satisfactory performance comes at the expense of a stringent acceleration demand in the early part of the engagement, which is typical of high-gain control.
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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) | 0.001 | 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 | 0.000 |
| 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".