Investigation, Flight Testing, and Comparison of Three Nonlinear Control Techniques with Application to a Quadrotor Unmanned Aerial Vehicle
Bibliographic record
Abstract
Unmanned Aerial Vehicles (UAVs) have become more and more popular, and how to control them through advanced control techniques becomes crucial. Although there are many different control methods that can be applied to the control of UAVs, nonlinear control techniques are more practical since the inherent nonlinear features of most UAVs. In this paper, three widely used nonlinear control techniques including Feedback Linearization Control (FLC), Sliding Mode Control (SMC), and Backstepping Control (BSC) are investigated, implemented and experimentally tested on a unique quadrotor UAV (known as Qball-X4) test-bed available at the Networked Autonomous Vehicles (NAV) Lab in Concordia University. The advantages and disadvantages of these three control techniques with application to the Qball-X4 UAV are revealed through both simulation and experimental tests. Sliding mode control is well known for its capability of handling uncertainties, and is demonstrated to be the most robust and best performance controller for Qball-X4. Feedback linearization control and backstepping control are demonstrated a bit weaker than sliding mode control. Comparison of these three controllers is also carried out in both theoretical analysis and experimental testing under same flight conditions. Testing results and comparison show the different features of different control methods and provide a view on how to choose an appropriate controller for controlling the Qball-X4 and other UAVs under a specific condition.
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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 | 0.001 |
| 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 | 0.000 | 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".