Adaptive Fault-Tolerant Control of Fixed-wing UAV Under Actuator Saturation and State Constraints
Bibliographic record
Abstract
This paper proposes a fault-tolerant control (FTC) scheme for unmanned aerial vehicles (UAVs) with state constraints, actuator saturation, and faults. Firstly, a nonlinear mapping function is designed to transform the limited states into new unlimited states. Furthermore, based on the transformed system, neural network (NN) is used to approximate the unknown nonlinear function caused by the parametric uncertainties, external disturbances, and actuator faults. Then, dynamic surface control (DSC) technique is used to solve the problem of “explosion of complexity”. Moreover, an auxiliary system is designed to avoid actuator saturation and a Nussbaum function is used to simplify solving the inverse of the matrix in the auxiliary system. Finally, the Lyapunov method is used to prove the correctness of the FTC scheme, and simulation results show the effectiveness of this scheme.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".