Adaptive model-free formation-tracking controller and observer for collaborative payload transport by four drones
Bibliographic record
Abstract
In this paper, a solution is provided for collaborative transportation of a rigid payload by four quadrotors, while a desired formation topology is maintained among the vehicles. Each quadrotor is connected to the payload using a rigid link, where the joints to the payload are located on two perpendicular lines through the center of mass of the payload. The proposed solution includes an adaptive control scheme comprising several modules. Each module is responsible for controlling the motion of a specific subsystem of the entire payload-links-quadrotors system. Since an adaptive model-free control algorithm is utilized for tracking the desired set-points for each module, no information on the inertial parameters of the drones is required. The formation topology is achieved among the quadrotors by a geometry-based solution. Moreover, by utilizing a special linear Kalman filter for estimating the unit vectors along the connecting rigid links, the requirement for position and velocity estimation of the drones is revoked. Instead, the position and velocity of the payload must be estimated by using appropriate sensors. The solution is validated via numerical simulation of transporting a payload along a time-varying trajectory.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".