Adaptive Anti-Disturbance Performance Guaranteed Formation Tracking Control for Quadrotor UAVs via Aperiodic Signal Updating
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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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) | 0.000 | 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 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".