Adaptive Set-Point Regulation using Multiple Estimators
Bibliographic record
Abstract
In this paper, we consider the problem of step-tracking for an n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> -order discrete-time plant with unknown plant parameters belonging to a closed and bounded uncertainty set; we naturally assume that the plant does not have a zero at z = 1. We carry out parameter estimation for a slightly modified plant; indeed, we cover the set of admissible parameters by a finite set of compact and convex sets, and use an original-projection-algorithm based estimator for each. At each point in time, a switching algorithm is used to determine which estimates are used in the pole-placement-based controller; our approach does not assume that the switching stops at any point in time. We prove that this adaptive controller guarantees desirable linear-like closed-loop behavior (exponential stability and a bounded noise gain), as well as asymptotic tracking when the noise is constant.
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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".