Time‐varying gain‐scheduling ‐error mean square stabilisation of semi‐Markov jump linear systems
Bibliographic record
Abstract
In this study, a time‐varying gain‐scheduling approach is proposed to deal with the problem of stabilisation for a class of semi‐Markov jump linear systems. A more general class of Lyapunov functions that depends not only on the system modes, but also on the staying time during the current system mode is constructed, which can cover the common time‐invariant Lyapunov functions as special cases. In the sense of the σ‐error mean‐square stability proposed previously, the numerically testable sufficient criteria for the stability analysis are derived and certain techniques are employed such that the obtained conditions are linear in the system matrices. Both the time‐invariant and time‐varying control syntheses are investigated, and the results in a recent study can be deemed as extreme cases of the obtained criteria. Finally, the developed theoretical results are verified by three numerical examples, and it is demonstrated that the results based on the time‐varying approach is less conservative than those based on the time‐invariant method.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".