Multirate Minimum Variance Control Design and Control Performance Assessment: A Data-Driven Subspace Approach
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Bibliographic record
Abstract
This paper discusses minimum variance control (MVC) design and control performance assessment based on the MVC-benchmark for multirate systems. In particular, a dual-rate system with a fast control updating rate and a slow output sampling rate is considered, which is not uncommon in practice. A lifted model is used to analyze the multirate system in a state-space framework and the lifting technique is applied to derive a subspace equation for multirate systems. From the subspace equation, the multirate MVC law and the algorithm are developed to estimate the multirate MVC-benchmark variance or performance index. The multirate optimal controller is calculated from a set of input/output (I/O) open-loop experimental data and, thus, this approach is data-driven since it does not involve an explicit model. In parallel, the presented MVC-benchmark estimation algorithm requires a set of open-loop experimental data and close-loop routine operating data. No explicit models, namely, transfer function matrices, Markov parameters, or interactor matrices, are needed. This is in contrast to traditional control performance assessment algorithms. The proposed methods are illustrated through a simulation example
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it