Design and Experimental Validation of Robust Self-Scheduled Fault-Tolerant Control Laws for a Multicopter UAV
Bibliographic record
Abstract
In recent years, multicopter unmanned aerial vehicles (UAV) have been widely used in many commercial and military applications. Due to the increasing requirement for high autonomy and safety, UAVs should possess a fault-tolerant ability to accommodate malfunctions during flight. This article presents two fault-tolerant control (FTC) designs for a multicopter UAV subject to actuator faults. The proposed FTC approach is based on gain-scheduling (GS) control in the framework of structured$\mathcal {H}_\infty$synthesis. The scheduled gains of the first controller are parameterized as polynomial functions of the loss of actuator effectiveness, given by an appropriate fault detection and diagnosis system. In order to facilitate the tuning process, the second controller uses the loss of virtual control effectiveness as the GS variable. Experimental results performed on an hexacopter UAV show the effectiveness and the robustness of these methods subject to multiple critical actuator faults.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".