Adaptive mixing formation control of multiquadrotor unmanned aerial vehicle systems
Bibliographic record
Abstract
Abstract This paper presents a distributed adaptive mixing control (AMC) design for formation maintenance of systems of multiquadrotor UAVs (q‐UAVs) during commanded path‐tracking maneuvers. The proposed formation control scheme has a two‐level structure. The high level defines the desired trajectories for rigid and persistent formation acquisition and tracking in 3D to maintain the predefined shape. At the low level, an indirect AMC law based on least‐squares (LS) parameter identification ensures accurate tracking and robustness to parametric uncertainties and disturbances in q‐UAV motion dynamics. The proposed scheme adaptively blends a set of pre‐designed scheduled linear quadratic control (LQC) gains, providing a smooth transition among operation point neighborhoods. The proposed scheme is also compared with a conventional adaptive LQC design. The stability analysis of the proposed control scheme is provided, and both the formation maintenance and path‐tracking performances are tested and compared through real‐time data‐based simulations.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".