Control of Quadcopter Drone Based on Fractional Active Disturbances Rejection Control
Bibliographic record
Abstract
In this paper, a fractional Active Disturbance Rejection Control (ADRC) is proposed to track the desired trajectory workspace and solve the attitude control problem of a quadcopter Unmanned Aerial Vehicle (UAV). The ADRC is a robust nonlinear control being used recently to control the UAV technologies. It is mostly used to solve the major key challenges related to uncertainties, imperfect modeling, and external disturbances in a simple structure and easy way in control parameters tuning. The main idea of the ADRC control strategy is to introduce a fictitious state variable that represents the total disturbance. Then, this disturbance is estimated via an Extended State Observer (ESO). The estimated disturbance is fed back to construct a suitable controller, and hence decouple the system from all uncertainties and disturbances acting on the plant. A fractional State Error Feedback (SEF) controller is implemented and compared with the traditional SEF to enhance the system performance. Simulation results demonstrate the robustness and effectiveness of the proposed fractional control strategy in tracking the desired trajectory in the workspace of the quadcopter UAV.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".