Finite-Time Adaptive Quantized Control for Quadrotor Aerial Vehicle with Full States Constraints and Validation on QDrone Experimental Platform
Bibliographic record
Abstract
The issue of finite-time stability has garnered significant attention in the control systems of quadrotor aerial vehicles. However, existing techniques for achieving finite-time control often fail to consider the system’s state constraint characteristics and rarely address input quantization issues, thereby limiting their practical applicability. To address these problems, this paper proposes a finite-time adaptive neural network tracking control scheme based on a novel barrier Lyapunov function for the quadrotor unmanned aerial vehicle (UAV) system. Firstly, an adjustable boundary for the barrier Lyapunov function is introduced in the control system of a quadrotor UAV, enabling convergence of all states within finite-time constraints during trajectory tracking. Subsequently, a filter compensation signal is incorporated into the recursive design process of the controller to mitigate errors caused by filtering. Finally, a smoothing intermediate function is employed to alleviate the impact of input quantization on the quadrotor system. Experimental validation is conducted on the Quanser QDrone experimental platform to demonstrate the efficacy of the proposed control scheme.
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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".