Containment control of discrete‐time multi‐agent systems with application to escort control of multiple vehicles
Bibliographic record
Abstract
Abstract This article investigates the escort control problem for heterogeneous discrete‐time multi‐agent systems with multiple leaders. We develop a distributed output feedback control law such that the followers are secured within the convex hull spanned by the output of the leaders. First, the followers estimate the target convex hull and the system matrices of the leaders via a distributed observer. We then devise a distributed dynamic output feedback control protocol based on this observer to achieve the escort control by using only neighboring relative output information of leaders. The security of the followers is guaranteed by seeing that the output tracking errors converge to zero exponentially. In the numerical simulations, we discuss the potential deployment of the proposed method to a brand‐new escort control of mixed traffic consist of connected automated vehicles and partially automated vehicles. This vehicular escort control formulation can be regarded as a generalization of the conventional vehicular platoon control problem into both longitudinal and lateral dimensions. The numerical results validate the effectiveness and the computational feasibility of the proposed control protocols.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".