Conditions for removing intersample ripples in multirate control
Why this work is in the frame
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Bibliographic record
Abstract
Multirate systems arise when signals of interest are sampled at different rates. Measurements from chemical processes are typically available at different sampling rates. For example, composition estimates in a distillation column are available at a much slower rate than flow, temperature and pressure measurements. Multirate systems pose a challenging problem due to several reasons such as increased complexity design, time-varying nature, etc. Systems consisting of fast-rate control moves and slow-sampled outputs are a common scenario in chemical processes and of practical interest. Traditionally, inferential techniques based on secondary measurements have been used to design the fast-rate input moves. Lifting techniques conveniently transform multirate systems to single-rate lifted systems with increased dimensionality. Controllers designed using lifting techniques require that certain causality constraints are satisfied. We show firstly, that intersample ripples can arise in the closed-loop output of a multirate system as a result of non-uniform gains of the discrete lifted system and inverse lifting. Secondly, we present conditions on the controller gains to avoid intersample ripples.
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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