Minimum variance in fast, slow and dual‐rate control loops
Bibliographic record
Abstract
Abstract In certain industrial applications, the control updating rate is faster than the output sampling rate by a certain factor, which leads to dual‐rate (DR) control problems. Generally speaking, a DR controller performs better than a slow single rate (SSR) controller but worse than a fast single rate (FSR) controller in the sense of minimum variance control. This conjecture is theoretically justified in this paper for a continuous linear time‐invariant (LTI) single‐input single‐output (SISO) system. The optimal FSR, DR and SSR controllers are designed under the same performance criterion: variance of the fast sampled output. The discretization of continuous stochastic disturbance models is investigated preserving certain basic statistical properties. A linear matrix inequality (LMI) approach is developed to calculate the optimal controllers for DR and SSR loops. The theoretical results are illustrated by two simulation examples. Copyright © 2005 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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".