Improvements to the Quasi-Static Feedback Linearization Algorithm with Application to a Slung Load System: Experimental Validation
Bibliographic record
Abstract
This paper presents an improved algorithm for Quasi-static feedback (QSF) and applies the design to trajectory tracking motion control for a multirotor Slung Load System (SLS) consisting of a drone and a simple pendulum pivoted at the drone’s mass centre. The simple pendulum carries a paylaod. The approach achieves an exact linearization of the tracking error dynamics. A simplified QSF control law is possible due to a geometric SLS model and an input transformation. The input transformation projects the geometric model to a local chart, which is defined everywhere except when the pendulum is horizontal. The exact error linearization has the benefit of a simplified stability condition and gain tuning. Experimental flight tests and software-in-the-loop (SITL) simulations are provided to validate the performance and robustness of the approach.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".