Distributed Formation Recovery Control of Heterogeneous Multiagent Euler–Lagrange Systems Subject to Network Switching and Diagnostic Imperfections
Bibliographic record
Abstract
This brief is concerned with the design of distributed formation recovery control laws for nonlinear heterogeneous multiagent Euler-Lagrange (EL) systems that are simultaneously subject to: 1) diagnostic information imperfections and unreliabilities; 2) parametric uncertainties and external disturbances; and 3) random switching of communication network topologies. The proposed recovery control techniques ensure both state synchronization and set-point tracking of a team of multiagent systems, while the agents have access only to local information. Our results are obtained for both fixed and switching communication network topologies. Distributed control recovery solutions for a general class of nonlinear multiagent EL systems have not been investigated earlier in the literature under the above simultaneous three realistic scenarios. The simulation results for the attitude control of a network of eight spacecraft tasked in a formation flying mission subject to communication topology switching demonstrate the effectiveness and capabilities of our proposed distributed recovery control strategies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".