Design and Implementation of a Hardware-in-the-Loop Simulation System for a Tilt Trirotor UAV
Bibliographic record
Abstract
The tilt trirotor unmanned aerial vehicle (UAV) is a novel aircraft that has broad application prospects in transportation. However, the development progress of the aircraft is slow due to the complicated control system and the high cost of the flight experiment. This work attempts to overcome the problem by developing a hardware-in-the-loop (HIL) simulation system based on a heavily developed and commercially available flight simulator X-Plane. First, the tilt trirotor UAV configuration and dynamic model are presented, and the parameters are obtained by conducting identification experiments. Second, taking the configuration of the aircraft into account, a control scheme composed of the mode transition strategy, hierarchical controller, and control allocation is proposed. Third, a full-scale flight model of the prototype is designed in X-Plane, and an interface program is completed for connecting the autopilot and X-Plane. Then, the HIL simulation system that consists of the autopilot, ground control station, and X-Plane is developed. Finally, the results of the HIL simulation and flight experiments are presented and compared. The results show that the HIL simulation system can be an efficient tool for verifying the performance of the proposed control scheme for the tilt trirotor UAV. The work contributes to narrowing the gap between theory and practice and provides an alternative verification method for the tilt trirotor UAV.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".