Fault-Tolerant Model Predictive Control of a Fixed-Wing UAV with Actuator Fault Estimation
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.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".