Modeling and Control of a Quadcopter Flying in a Wind Field: A Comparison Between LQR and Structured ℋ<sub>∞</sub> Control Techniques
Bibliographic record
Abstract
This article deals with the stationary flight control problem of an Unmanned Aircraft Vehicle (UAV) flying in a wind field. The main objective is to develop a robust control law to stabilize the drone flying under real-life outdoor conditions while maintaining adequate flight performances. To do so, a generic nonlinear dynamic model of the quadcopter is firstly developed; this model is then completed with the modeling of the wind disturbances, which allows the simulation of the proposed control algorithms. Two approaches for the synthesis of the control laws are compared: the first one uses Linear Quadratic Regulator (LQR) synthesis and the second one uses structured H∞synthesis. Simulations are conducted to evaluate the performances of both control laws when subjected to a nominal wind step input varying from 0 to 14 m/s. This particular input choice makes it possible to analyze the performance of the controllers in both transient (wind gust) and steady states (sustained wind). The results show that better performances are obtained with the structured H∞synthesis using the robust control theory than with the LQR synthesis using the optimal control theory. Furthermore, it is shown that the simplicity of use of the Robust Control Toolbox of MATLAB favors the usage of more complex control architectures without impacting the workload of the control engineer.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".