Robust Actuator Fault Detection and Diagnosis for a Quadrotor UAV With External Disturbances
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 it