Bibliographic record
Abstract
Applying the Lie derivative concept, we propose a new control method of generalized common model control (GCMC) by generalizing the CMC algorithm to complex processes with the relative order larger than 1 to overcome the disadvantage of common model control (CMC), i.e., it can only apply to the processes with relative order of 1. The model of the non-linear controlled plant is directly embedded in the controller, so that the controlled non-linear system is a linear high order system in the no constrained control input, it is very easy to tune for the controller by applying the method of dominant poles. The simulation results show that the general common model controller is very effective for the non-linear system. En appliquant le concept dérivatif de Lie, on propose une nouvelle méthode de régulation à modèle commun généralisé (GCMC) en généralisant l'algorithme CMC à des procédés complexes d'ordre relatif plus grand que 1 pour contourner l'inconvénient du contrôle de modèles commun (CMC), à savoir qu'il ne peut s'appliquer qu'à des procédés d'un ordre relatif de 1. Le modèle de contrôle non linéaire d'usine est directement appliqué au contrôleur, de sorte que le système non linéaire contrôlé est un système d'ordre élevé linéaire en entrée de contrôle non contraint et qu'il est très facile de régler le contrôleur par la méthode des pôles dominants. Les résultats des simulations montrent que le régulateur à modèle commun généralisé est très efficace pour le système non linéaire.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".