Fault-Tolerant Model Predictive Control of a Fixed-Wing UAV with Actuator Fault Estimation
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Bibliographic record
Abstract
The vast majority of today’s engineering systems possess operational constraints and have multiple inputs and outputs. This classifies them as Multi-Input Multi-Output (MIMO) systems. This paper develops a novel observer-based fault diagnosis scheme with the capability of simultaneous state and actuator fault estimation for Linear Time-Invariant (LTI) MIMO systems, which is then integrated with Model Predictive Control (MPC) method for achieving fault-tolerant control. The application within this study is chosen to be the longitudinal flight control of a fixed-wing Unmanned Aerial Vehicle (UAV). The observer-based method is combined with two MPC schemes to detect and compensate randomly occurring actuator faults in real time. The faults are modeled as a Loss Of Effectiveness (LOE). For the first (efficient) MPC method, a simple reconfiguration can be performed in the event of faults, as it is based on an absolute input formulation. However, as the second (integral-action) MPC is based on an incremental input formulation, reconfiguration is not required, since this algorithm has a degree of implicit fault tolerance. Numerical simulations demonstrate the effectiveness of the proposed approach for both MPC schemes.
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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