Bibliographic record
Abstract
Four-rotor UAV is a type of small aircraft. Because of its flexibility, stability, good maneuverability and other characteristics, it can be widely used in various fields. Attitude control of four-rotor UAV is a core technology in the field of UAVs, which is worth studying. In this paper, previous research progress in the field of attitude control of four-rotor UAV is reviewed. The main control methods can be divided into linear control, nonlinear control and intelligent control. In this paper, PID control, sliding mode control, backward step control, intelligent control, neural network control and fuzzy control are introduced in detail, and the characteristics, advantages, disadvantages and research status of these different attitude control technologies are summarized and analyzed. Relevant references show that the focus of researches in resent five years is to combine a variety of control technologies, improve the traditional control technology to obtain better control effects, and use MATLAB/Simulink simulation to draw conclusions. Then, the research on attitude stability control technology of four-rotor UAV in response to adverse weather environment is analyzed, and some future research directions are proposed, including but not limited to establishing a reasonable environmental disturbance model, improving UAV dynamics model in wind field environment, developing fault-tolerant control technology and reducing the delay of system response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".