Event‐Triggered Trajectory Tracking Control for Quadrotor UAVs Subject to External Disturbances
Bibliographic record
Abstract
ABSTRACT This article investigates the trajectory tracking control problem for quadrotor UAVs using the dynamic event‐triggered control approach. Unlike existing results, the dynamic event‐triggered control strategy proposed in this work ensures that the trajectory tracking error of quadrotor UAVs converges to zero asymptotically for a class of external disturbances. Specifically, an event‐triggered mechanism is introduced in the position loop to reduce the resource transmission consumption. To address the non‐differentiable nature of the event‐triggered signal, a fourth‐order linear system model for the position loop is derived, ensuring the existence of a twice‐differentiable acceleration reference which is essential for the attitude loop. Subsequently, based on the internal model principle, we develop a class of dynamic event‐triggered control strategies with dynamic triggering mechanisms. Furthermore, to handle the challenges posed by the unknown parameters and external perturbations within the attitude‐loop subsystem, a robust adaptive dynamic control law is implemented based on the attitude rotation matrix. Rigorous Lyapunov analysis demonstrates that the overall control approach ensures asymptotic stability of the closed‐loop system. Finally, we verify the effectiveness and robustness of the controller through numerical simulations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".