DUKF-based GTM UAV fault detection and diagnosis with nonlinear and LPV models
Bibliographic record
Abstract
Fault tolerant control system (FTCS) is to maintain system stability in the presence of fault occurred in the actuators, sensors or other system components. When a fault/failure occurs either in an actuator, sensor or plant, the fault detection and diagnosis (FDD) as the central part of a FTCS will detect and diagnose the source and the magnitude of the fault. The reconfiguration scheme in the FTCS will design the reconfigurable controller based on the FDD information to balance and adapt the system for being able to tolerate to the faults and failures. This paper presents an applicable procedure of integrated fault detection and diagnosis for the NASA Generic Transport Model (GTM) unmanned aerial vehicle (UAV) aircraft model. The implementation of a FDD scheme in handling partial control effector fault cases with the nonlinear GTM and the linear parameter varying (LPV) representation of the nonlinear GTM have been carried out based on a dual unscented Kalman filter (DUKF) and a Baysian rule for detection and isolation decision making. Simulation results show satisfactory results for detecting and diagnosing the control effectors failures.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".