Switched Model Predictive Control with Scheduled Mode Transitions without Terminal Constraints
Why this work is in the frame
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Bibliographic record
Abstract
This paper studies the switched model predictive control (MPC) problem for a class of constrained discrete-time switched linear systems with the minimum dwell-time restriction. According to the known information of the predesigned admissible switching sequences, the switched MPC problem is modified by taking into consideration the real-time updated truncated admissible switching (TAS) sequences so as to optimize the input actions with improved performance. Instead of using the conventional terminal constraints to ensure the closed-loop stability, a sufficient condition on the prediction horizon is derived under general assumptions to achieve the recursive feasibility and asymptotic stability of the closed-loop system. Furthermore, the algorithm which is employed to calculate the constrained dwell-time invariant (CDI) set is slightly modified to accommodate the input constraint. Based on the algorithm, the suboptimal estimated parameters are quantitatively gauged. The simulations are given to verify the theoretical results.
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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