Robust optimal distance‐based formation control of uncertain nonlinear agents over directed topologies
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Bibliographic record
Abstract
Abstract This paper studies a formation control problem with distance constraints for nonlinear agents with uncertainties. Controlling an edge is assigned to only one of its incident agents. Directed graph theory is used to model the desired formation topology. The method is distributed, applicable to uncertain nonlinear agents, and is based on robust‐optimal control. The proposed control scheme is based on integral sliding mode control (ISMC) combined with the state‐dependent Riccati equation (SDRE) method. The method minimizes a weighted cost function that includes the formation and control input costs for a given mission while compensating for the effect of uncertainties. A rigorous Lyapunov stability analysis proved the local asymptotic convergence of the agents to the desired distances. We use the concept of mathematical induction to show that the formation of all agents is stable. Detailed simulation results are included to verify the proposed control scheme.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it