Robust Distributed Control of Networked Systems with Linear Programming Objectives /
Bibliographic record
Abstract
The pervasiveness of networked systems in modern engineering problems has stimulated the recent research activity in distributed control. Under this paradigm, individual agents, having access to partial information and subject to real-world disturbances, locally interact to achieve a common goal. Here, we consider network objectives formulated as a linear program where the individual agents' states correspond to components of the decision vector in the optimization problem. To this end, the first contribution we make is the development of a robust distributed continuous-time dynamics to solve linear programs. We systematically argue that the robustness properties we establish for this dynamics are as strong as can be expected for linear programming algorithms. The next contribution we make is the design of a distributed event-triggered communication protocol for the aforementioned algorithm. We establish various state- based rules for agents to determine when they should broadcast their state, allowing us to relax the need for continual information flow between agents. Turning our attention to a specific network control problem for which our algorithm can be applied, we consider distributed bargaining in exchange networks. In this scenario, agents autonomously form coalitions of size two (called a match) and agree on how to split a payoff between them. We emphasize fair and stable bargaining outcomes, whereby matched agents benefit equally from the collaboration and cannot improve their allocation by unilaterally deviating from the outcome. We synthesize distributed algorithms that converge to such outcomes. Finally, we focus on cooperation-inducing mechanisms to ensure that agents in a bargaining outcome effectively realize the payoff they were promised. As an illustrative example, we study how to allocate the leader role in unmanned aerial vehicle (UAV) formation pairs. We show how agents can strategically decide when to switch from leading to following in the formation to ensure that the other UAV cooperates. Throughout the thesis, we emphasize the development of provably correct algorithms, making use of tools from the controls systems community such as Lyapunov analysis and the Invariance Principle. Simulations in distributed optimal control, multi-agent task assignment, channel access control in wireless communication networks, and UAV formations illustrate our results
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".