A Robust Distributed MPC Framework for Multiagent Consensus With Communication Delays
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".