Robust distributed model predictive control of constrained continuous-time nonlinear systems using two-layer invariant set
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Bibliographic record
Abstract
This paper investigates the distributed model predictive control (MPC) of a group of dynamically decoupled nonlinear systems coupled by cost function. The communication topology features neighbor-to-neighbor information exchange of the assumed system state trajectory. The cooperation among dynamically decoupled nonlinear systems is promoted by including a coupling term in the cost function. A control strategy is designed based on the two-layer invariant set in order to handle the effect of the external disturbances. It is shown that by appropriately choosing the sampling interval, the recursive feasibility is guaranteed provided that the initial state is feasible and the disturbance is bounded by a certain level. Sufficient conditions are established such that all system states converge to their corresponding robust positively invariant sets. The effectiveness of the proposed method is verified through a simulation example.
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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) | 0.001 | 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