Event‐triggered robust model predictive control for linear discrete‐time systems with a guaranteed average inter‐execution time
Why this work is in the frame
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Bibliographic record
Abstract
Abstract An event‐triggered robust model predictive control (MPC) approach is proposed for linear discrete‐time systems with bounded disturbances. According to the probability distribution of bounded disturbances, an event‐triggered scheme involving a designed minimal robust positively invariant set is constructed to generate dynamic triggering sets. The MPC‐related optimization problem subject to hard constraints should be solved only at event‐triggered instants when the state is outside the corresponding triggering set. A classical tube‐based MPC that allows the initially predicted state different from the current actual state of the plant is considered to improve the feasible region. The designed event‐triggered controller can achieve a prescribed expectation of inter‐execution times and reduce the burden of communication and computation, while not sacrificing the quadratic performance significantly. It is proved that the proposed control approach ensures recursive feasibility and robust stability. Three examples are used to demonstrate the effectiveness and advantages of the proposed method.
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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