Adaptive Anti-Disturbance Performance Guaranteed Formation Tracking Control for Quadrotor UAVs via Aperiodic Signal Updating
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Bibliographic record
Abstract
In order to realize the operability and safety of unmanned aerial vehicles (UAVs) in confined areas, this article investigates an adaptive anti-disturbance performance guaranteed fuzzy formation control problem for quadrotor UAVs by using aperiodic signal updating. The unknown dynamics are approximated by using fuzzy logic systems. A disturbance observer is constructed for each UAV, including position subsystem (outer-loop) and attitude subsystem (inner-loop), to reduce the negative effects of UAVs with disturbances in complex flight environments. To avoid the potential internal collision among the multiple UAVs, a prescribed performance function that widens the initial value range of the consistency error is designed to keep the formation error within the specified range. Intermittent output signals generated by event-triggered control strategy of attitude subsystem are used to reduce sensors data transmission on each UAV, thereby saving energy and communication resources. Via the Lyapunov stability theory, the formation error can converge to a prescribed boundary range. Finally, the validity of the proposed control strategy is illustrated by simulation results.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it