Robustification d'une commande GPC par optimisation convexe du paramètre de Youla
Bibliographic record
Abstract
This paper presents a methodology for enhancing the robustness of a GPC controlled SISO system by convex optimisation of the Youla parameter. This methodology requires as a first step the design of an initial GPC controller; this controller is then robustified considering temporal and frequency constraints. In this way, a compromise between robustness and closed loop behaviour can be easily managed By means of the Youla parametrization, temporal and frequency constraints are formulated within a convex optimisation framework and the optimal parameter is obtained by solving this optimisation problem. As a starting point, the robustness of the controller regarding model uncertainties in high frequency is enhanced while respecting a temporal template for the disturbance rejection. Finally, this method is generalized in such a way that the Youla parameter enables the change of different transfers of the system, for robustification purposes as well as dynamic adjustment of the system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".