An agile single board quadrotor providing “eye in the sky” capabilities for marine environments
Bibliographic record
Abstract
This paper presents a robust model independent control method for the stabilization of a four-rotor rotorcraft, which is popularly known as the quadrotor helicopter. In addition, a detailed dynamic model is presented for the formulation of the proposed control law and a prototype platform heavily utilizing printed circuit board technology is proposed for experimentation. This paper focuses on compensating for unknown disturbances (e.g., wind gusts) that naturally occur in a marine environment. In order to employ eye in the sky capabilities in a marine environment, a quadrotor helicopter must hand these disturbances without jeopardizing the stability of the vehicle. To this end, the control law is derived from the Active Disturbance Rejection Control (ADRC) technology, which is reported in the literature to be resistant to external disturbances. The robustness of the proposed controller is demonstrated through numerical simulation of the vehicle's vertical flight. External disturbances in the simulation experiment is considered as a sudden vertical gust of wind acting on the vehicle. In order to quantify the robustness of the controller relative to conventional control technologies, a PD controller under identical flight conditions provides a reference benchmark. The comparative performance of the two controllers shows that the proposed control algorithm significantly outperforms the PD algorithm under the simulated disturbance conditions.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".