Distributed Model Predictive Control for Tracking Consensus of Linear Multiagent Systems With Additive Disturbances and Time-Varying Communication Delays
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Bibliographic record
Abstract
In this article, we investigate a robust distributed model predictive control (DMPC) scheme for tracking the consensus of linear multiagent systems (MASs) subject to additive disturbances and time-varying communication delays. A terminal constraint set is constructed by the Lyapunov-Razumikhin functional, and a corresponding local controller is designed for each agent. Furthermore, the sufficient conditions ensure that the terminal constraint set is provided in the form of linear matrix inequalities (LMIs). The recursive feasibility of the proposed algorithm is guaranteed based on the designed terminal constraint set, terminal cost, and local controller. Moreover, the closed-loop system is shown to be input-to-state stable (ISS). An illustrative example is given to verify the effectiveness of the presented approach.
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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