A Comprehensive Review and Applications of Active Disturbance Rejection Control for Unmanned Aerial Vehicles
Bibliographic record
Abstract
Over the past few decades, there has been a consistent interest in the creation and use of Unmanned Aerial Vehicles (UAVs). Although originally developed for military purposes, such as surveillance and target acquisition, UAVs are now being utilized in a variety of fields, including tourism, public safety, transportation, and healthcare. Due to the considerable interest in the use of UAVs and their complex dynamic behavior, there has been a growth in the design and practical implementation of different control methods to accomplish their tasks and missions successfully. Control approaches developed for UAV systems mainly include adaptive control, robust control, and Active Disturbance Rejection Control (ADRC). Recently, ADRC has gained significant popularity as a control method for UAVs due to its robustness against uncertainties and disturbances, as well as its ease of implementation. This review paper aims to provide a comprehensive evaluation and insightful look into the various ADRC structures developed for UAV systems, as well as to highlight the basic issues involved in this field. This will allow readers to identify potential future requirements for expanding the utility of UAVs. An illustrative example of the ADRC scheme in the Parrot Mambo quadcopter is also included in this review paper.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".