Sampled-data GPC (SDGPC) with integral action: the state space approach
Bibliographic record
Abstract
In this paper, a sampled-data generalized predictive control (SDGPC) algorithm is developed. SDGPC is based on a continuous-time state space model with continuous-time quadratic cost function, but the projected future control scenario is assumed to be piecewise constant. In doing so, SDGPC can be implemented digitally without any approximation. By state augmentation, SDGPC produces integral action to track a constant setpoint with zero steady error subject to an unknown constant disturbance. Laguerre filter modeling concepts which have been popular recently in process industry can be integrated into this controller design readily and the resulting sampled-data Laguerre-based GPC (SDLGPC) is suitable for adaptive applications. The closed-loop stability of SDGPC is established and the relation between SDGPC and the discrete-time approach is analyzed. Some simulation examples are presented to illustrate the properties of SDGPC.>
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".