H<inf>&#x221E;</inf> control design for uncertain linear systems with time-varying delays using LMI
Bibliographic record
Abstract
A new delay-dependent approach for robust control of multiple time-varying delayed systems with uncertain parameters is proposed. The internal stability of the proposed controller is shown by proposing a new Lyapunov-Krasovskii functional. The upper bound of the delay and its time-derivative are explicitly used in designing the controller. No constraint is imposed on the time-delay functions. Hence, the closed-loop system performance is more robust and less conservative in terms of tolerating the effects of time-varying delays. Moreover, the proposed controller does not rely on restrictive assumptions on the rate of change of time-delay function, (i.e. \tau\ < 1), which makes it applicable to fast time varying delayed systems. Finally, a robust state feedback controller is designed via Linear Matrix Inequality (LMI) technique. Unlike many previous methods, no parameter tuning is necessary to solve the resulting LMI conditions. Simulation results confirm that our proposed controller yields results that are more robust and less conservative as compared to existing methods in the literature.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.018 | 0.006 |
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".