Evaluation of Fractional Proportional-Integral-Derivative and Integer Proportional-Integral-Derivative Control for a Drone-Based Cable Suspended Payload System
Bibliographic record
Abstract
Abstract Arctic remote sensing is crucial for studying and preserving the vulnerable Arctic environment. Researchers use ground penetrating radars (GPRs) to understand climate change and ice properties, but maneuvering a bulky GPR on the inhospitable Arctic terrain is difficult. Suspending the GPR from a drone can be a solution to operating a large GPR, however, proper control and stabilization of the drone-GPR system pose a challenge. In this paper, we contribute to the literature by analyzing the response of closed-loop feedback fractional-order and integer-order proportional-integral-derivative (PID) controllers at minimizing the payload sway for a drone-based cable-suspended payload system. Both controllers were tuned using particle swarm optimization and simulated to experience delays and external disturbances. Results indicate that both controllers had comparable time responses. The fractional-order controller was more robust to disturbances but was also sensitive to system delays. The findings of this study can be considered for future drone development.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".