MPC-based FTC with FDD against actuator faults of UAVs
Bibliographic record
Abstract
The increasing development of unmanned aerial vehicles (UAVs) and the requirements of high autonomy and safety levels require that the controller of UAVs should possess fault-tolerant/reconfigurable function to accommodate unpredicted situations such as actuator faults, sensor faults, or aircraft damage. This paper addresses the partial loss of control effectiveness (LOE) of actuators in a quadrotor UAV using model predictive control (MPC) with terminal constraints, which allows to track a reference command even if in the presence of actuator faults. The proposed fault-tolerant control system (FTCS) adopts MPC technique to design fault-tolerant controller and state-augmented Kalman filter (SAKF) to achieve fault detection and diagnosis (FDD) function. Simulation results based on a quadrotor UAV demonstrate that the proposed fault-tolerant controller has a good performance in accommodating actuator faults.
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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".