Robust predictive control of switched systems: Satisfying uncertain schedules subject to state and control constraints
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Bibliographic record
Abstract
Abstract In this work, we consider robust predictive control of switched uncertain nonlinear systems required to satisfy a prescribed switching sequence with uncertainty in the switching times subject to state and input constraints. To illustrate our approach, we consider first the problem of satisfying a prescribed schedule subject to uncertainty only in the switching times. Predictive controllers that guarantee the satisfication of state and input constraints from an explicitly characterized set of initial conditions are first designed. The performance and constraint‐handling capabilities of the predictive controllers are subsequently utilized in ensuring the satisfaction of the switching schedule while preserving stability. The results are then generalized to address the problem in the presence of parametric uncertainty and exogenous time‐varying disturbances in the dynamics of the constituent modes. The proposed control method is demonstrated through application to a scheduled chemical process example. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it