A Robust Distributed MPC Framework for Multiagent Consensus With Communication Delays
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Bibliographic record
Abstract
This paper addresses the consensus problem of linear discrete-time Multi-Agent Systems (MASs) under the conditions of input constraints and bounded time-varying communication delays. We propose a novel consensus framework for such constrained MASs that incorporates an offline optimal consensus design for unconstrained systems to achieve optimal consensus convergence, along with an online robust Distributed Model Predictive Control (DMPC) to accommodate constraints. Our framework accomplishes near-optimal consensus performance by minimizing the divergence between the online DMPC input and the pre-designed optimal consensus input, all while adhering to control input constraints. Notably, we explicitly integrate the knowledge of communication topology into the offline consensus protocol design, thereby enhancing the analysis of consensus convergence in MASs. More specifically, each agent is equipped with an offline consensus protocol based on the estimated states of its immediate neighbors. Furthermore, we demonstrate that estimation errors propagated over time due to imprecise neighboring information, remain bounded under mild assumptions. In addition, we confirm that with the appropriate design of the cost function and constraints, the feasibility of the related optimization problem can be recursively assured. We also provide a consensus convergence result for the constrained MASs under conditions of bounded varying delays. Lastly, we present two numerical examples that verify the effectiveness of the proposed distributed consensus algorithm.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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