Research on Visual Flight of Cascade Control of Quadrotor UAV Based on Coordinate Transformation
Bibliographic record
Abstract
Aiming at the problem that it is difficult for quadrotor UAV to directly observe the real-time flight posture and debug the flight control algorithm when flying at high altitude, the structural relationship and motion analysis of quadrotor UAV are established based on coordinate transformation theory, and the cascade control algorithm of PID and backstepping controller is adopted, and the flight control of quadrotor UAV is simulated and verified visually by Solidworks and Matlab/Simscape. In order to analyze and compare, the body model of quadrotor UAV is built under Simulink and Simscape respectively, and an 8-shaped expected flight trajectory based on trigonometric function is designed. The experimental results show that the quadrotor UAV body constructed by Simulink and Simscape respectively has the same flight effect under the control of the same algorithm. Generally, only numerical simulation results are obtained under Simulink. Under Simscape, not only numerical results can be obtained, but also 3D visual simulation results can be given. The flight ability of quadrotor UAV is related to the control algorithm and the expected tracking trajectory, and the expected control goal can be achieved by selecting different control algorithms or adjusting the parameters of the control algorithm. The tracking trajectory flight effect of quadrotor UAV is closely related to the response time. The control algorithm with shorter response time has better tracking effect. The response time of the control algorithm in this paper is short, and the tracking effect is good. The simulation system in this paper is intuitive, accurate and reliable, and can study and test the control algorithm well, which is of great value to the research of quadrotor UAV and control algorithm.
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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) | 0.001 | 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 | 0.001 |
| 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".