Precision Parameter Identification in Quadcopter UAV Systems Using Particle Swarm Algorithm
Bibliographic record
Abstract
This paper presents a method for precise parameter identification in quadcopter Unmanned Aerial Vehicle (UAV) systems using the Particle Swarm Optimization Algorithm (PSO).Accurate identification of dynamic parameters such as thrust, drag coefficients, and moments of inertia is essential for ensuring stable and responsive flight control.The proposed approach employs the PSO algorithm to optimize these parameters by minimizing model fitting errors, using simulation and experimental data.The identification was conducted under closed-loop conditions, due to the inherent instability of quadcopter UAVs.The performance of this approach was validated using simulations performed on the obtained model of the quadcopter, which were compared with real data obtained from real-world experiments.The results demonstrate significant improvements in model accuracy, with enhanced control precision and trajectory tracking performance.The method shows great potential for UAV system identification and control design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".