Fault-Tolerant Control of a Hexacopter UAV based on Self-Scheduled Control Allocation
Bibliographic record
Abstract
In this paper, a control allocation (CA) algorithm for the fault-tolerant control (FTC) system of a multirotor unmanned aerial vehicle (UAV) is presented. The proposed CA design is based on gain-scheduling control in the framework of structured H∞synthesis. Given the appropriate fault detection and diagnosis (FDD) system, allocation laws are parameterized as polynomial functions of actuators effectiveness levels. The polynomial coefficients are then tuned with MATLAB-based function systune to meet the robustness and fault tolerance requirements. Unlike conventional CA schemes in the literature, the proposed method has the advantage of not solving any online optimization problem. Moreover, actuator constraints after a failure are also taken into account during the tuning process using a multi-model approach. In comparison with other FTC systems based on reconfigurable controller, this smooth self-scheduled CA has a very simple structure and allows one to avoid undesirable transient phenomena during the controller reconfiguration process. High fidelity simulations and experimental results performed on a hexacopter show the effectiveness and robustness of the proposed FTC in accommodating different levels of actuator degradation.
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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 | 0.000 | 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) | 0.001 | 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".