Robust Actuator Fault Detection and Diagnosis for a Quadrotor UAV With External Disturbances
Bibliographic record
Abstract
This paper presents a robust actuator fault detection and diagnosis (FDD) scheme for a quadrotor UAV (QUAV) in the presence of external disturbances. First, the dynamic model of a QUAV taking into account actuator faults and external disturbances is constructed. Then, treating the actuator faults and external disturbances as augmented system states, an adaptive augmented state Kalman filter (AASKF), is developed without the need of make the assumption that the exact stochastic information of actuator faults and external disturbances are available. Next, in order to reduce the computational load of AASKF, an adaptive three-stage Kalman filter (AThSKF) is proposed by decoupling the AASKF into three subfilters. The AThSKF-based FDD scheme can not only detect and isolate actuator faults but also estimate the magnitudes even if the QUAV suffers from the external disturbances. Finally, the performance of the FDD scheme is evaluated under different fault scenarios, and simulation results demonstrate the effectiveness of the proposed method.
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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 | 0.001 | 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".