Adaptive regulation in bimodal linear systems
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Bibliographic record
Abstract
Abstract This paper considers an adaptive regulation problem for switched bimodal linear systems where it is desired to achieve regulation against unknown sinusoidal exogenous inputs representing disturbance or reference signals. Switching among plant models as well as among disturbance and reference signals is defined according to a performance variable. The design of the proposed adaptive regulators involves two main steps. First, a set of observer‐based Q ‐parameterized stabilizing controllers for the switched system is constructed, and a sufficient regulation condition for the resulting switched closed‐loop system is presented. Second, an adaptation algorithm is developed to tune the Q parameter in the expression of the parameterized controller and make it converge to the desired Q parameter that guarantees regulation for the switched system. The tuning of the Q parameter is performed to compensate for the lack of information on the properties of the sinusoidal exogenous inputs. Finally, a numerical example is presented to illustrate the effectiveness of the proposed method. Copyright © 2009 John Wiley & Sons, Ltd.
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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 it