Observer-Based Event-Triggered Active Disturbance Compensation Control and Its Applications to QUAV
Bibliographic record
Abstract
This article investigates a class of nonlinear systems subject to both uncertainties and external disturbances and proposes a compensation function observer-based event-triggered active disturbance compensation control (CFO-ETADCC). The control law incorporates the disturbance estimate generated by the CFO, and the control signal is transmitted to the system only at event-triggered instants. Based on the ETADCC framework, an additional event-triggered mechanism is designed for the measurement signals, upon which an event-triggered CFO-based active disturbance compensation control method (ETCFO-ETADCC) is proposed. The introduction of dual event-triggering mechanisms enables intermittent transmission of both measurement and control signals, thereby improving the efficiency of communication resource utilization. Through Lyapunov stability theory, the boundedness of the observer error and the stability of the closed-loop system are proven. The effectiveness of the proposed methods is validated through simulations and experiments for quadrotor uncrewed aerial vehicle attitude tracking control.
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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".