Distributed Time-Varying Output Formation Tracking Control for General Linear Multi-Agent Systems With Multiple Leaders and Relative Output-Feedback
Bibliographic record
Abstract
In this paper, a unified distributed swarm intelligence algorithm is developed to study time-varying output formation (TVOF) for a general linear multi-agent system (MAS) with a directed network. New adaptive output-feedback formation protocols are proposed to achieve TVOF stabilization for leaderless directed networks and TVOF tracking for leader-follower networks. For the leaderless case, only agents’ outputs are required to achieve the desired time-varying formation. An adaptive observer-type formation protocol is constructed via relative outputs of neighboring agents. No global information of the directed network is used to determine the protocol. A distributed algorithm is developed to solve the TVOF stabilization problem after the observability decomposition. For the leader-follower case, only partial agents have knowledge of the leaders’ information. An adaptive formation tracking protocol is constructed using dynamic relative output-feedback for neighboring followers. Based on the distributed algorithm, it is proved that the TVOF tracking problem with multiple leaders can be solved in a fully distributed manner.
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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 | 0.001 |
| 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".