Passivity-Based Control of Multiple Quadrotors Carrying a Cable-Suspended Payload
Bibliographic record
Abstract
In this article, a new passivity-based controller is proposed for stable cotransportation of a cable-suspended payload by a number of quadrotors. Nominal proportional–derivative position controllers with desired acceleration feedforward are augmented with time-varying dissipative terms to account for the drones’ underactuation. A storage function is defined that includes terms inspired by the kinetic and potential energies of the system components as well as virtual energy of the controller. A time-domain passivity observer is used in conjunction with an adaptive dissipative term to ensure that the value of the storage function is continuously decreased and, hence, guarantees closed-loop stability. Moreover, barrier Lyapunov functions are employed to avoid interdrone collisions. The proposed controller makes no assumption about and requires no knowledge of the cables’ tension status. Additionally, it requires no measurement from the payload. The cables can be attached to the quadrotors and the payload at arbitrary points. Experiments with a system of three quadrotors and a cable-suspended payload demonstrate the effectiveness of the proposed approach.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".