Bibliographic record
Abstract
Abstract This paper considers the regulation problem for bimodal systems against known disturbance and reference signals. Switching between the two plant models as well as between the disturbance and reference signals is defined according to a switching surface. The design of the proposed regulators involves three main steps. First, a set of observer‐based Q‐parameterized stabilizing controllers for the switched system is constructed. The stability and the input/output properties for the resulting closed‐loop switched system with the Q‐parameterized controllers are analysed. Second, regulation conditions for each of the two subsystems in the resulting bimodal switched closed‐loop system are presented. In the third step, regulation conditions for the switched closed‐loop system are developed using two approaches. In the first approach, sufficient regulation conditions are derived based on the closed‐loop system's input–output properties. In the second approach, the forced switched closed‐loop system is transformed into an unforced impulsive switched system using an appropriate coordinate transformation. Hence, the regulation problem for the switched closed‐loop system is transformed into a stability analysis problem for the origin of an impulsive switched system. A regulator synthesis method based on solving some linear matrix inequalities is proposed. Finally, a numerical example is presented to illustrate the effectiveness of the proposed method. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.003 | 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".