INTEGRATION OF SENSOR AND ACTUATOR FAILURE DETECTION, IDENTIFICATION, AND ACCOMMODATION SCHEMES WITHIN FAULT TOLERANT CONTROL LAWS
Bibliographic record
Abstract
This paper describes the design of a fault tolerant scheme for coping with both sensor and actuator failures within a flight control system. The failure detection and identification scheme is based on the use of neural estimators interfaced with correlation functions of the aircraft angular rates. Particular emphasis is placed on the differentiation between sensor and actuator failures. The failure types considered are actuator blockage along with partial/total loss of aerodynamic efficiency of the control surface and angular rate sensor step-type failure. The design of the accommodating control laws for actuator failures is based upon a non-linear dynamic inversion approach with neural network augmentation. The accommodation of sensor failures is performed by replacing the failed sensor output with neural estimates computed as part of the detection and identification process. The performance of the scheme is evaluated using the non-linear simulator for the NASA Intelligent Flight Control System F-15 aircraft developed at West Virginia University. The simulation results confirm the capabilities of the scheme to handle both sensor and actuator failures of different types over a large range of magnitudes.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".