A multi-objective control approach for the synthesis of robust digital guidance laws
Bibliographic record
Abstract
This paper proposes a new approach for the synthesis of robust digital guidance laws with the objective of achieving stable and accurate missile guidance despite parametric uncertainties in the missile flight control system dynamics, prescribed limits on missile acceleration, and digital implementation at possibly slow sampling rates. The proposed approach is characterized by two consecutive steps. Firstly, a robust continuous-time guidance law is designed using mixed H 2 -H∞ minimization and pole placement such that the effects of noise and parametric uncertainties are attenuated. To carry out this first step of the approach, missile flight control dynamics are modelled as second-order interval transfer functions, where bounded time-varying parameters characterize the missile flight envelope. Secondly, digital redesign of the robust continuous-time missile control system (including guidance and flight control) is performed by solving an optimal control problem. The proposed global digital redesign strategy results in robust performance for the closed-loop sampled-data missile control system for a wider range of sampling rates than those obtained with currently available approaches and can be readily implemented on commercially available software by following the step-by-step procedure described in the paper. Numerical simulations consisting of a missile pursuing a manoeuvring target, described by the so-called Singer model, demonstrate the effectiveness of the proposed approach.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".