Finite-Time Fractional-Order Neural Adaptive Fault-Tolerant Control of High-Speed UAV with Redundant Second-Order Actuators
Bibliographic record
Abstract
To address the safety control problem of high-speed unmanned aerial vehicle (UAV) with redundant actuators, this paper investigates the finite-time fault-tolerant control scheme of high-speed UAV under actuator faults. In the proposed scheme, tracking errors are transformed by a proportional-integral-derivative (PID) filter. Then, a composite learning algorithm with neural network and finite-time disturbance observer is used to estimate the transform errors and external disturbances. Considering the second-order actuator dynamics, the virtual control variable and the fault-tolerant control law are designed by using the fractional-order (FO) sliding mode control (SMC) strategy. The adaptive neural network is used to estimate the unknown term induced by model uncertainties. Furthermore, by using the Lyapunov stability analysis, it is shown that UAV can track the desired attitudes and the tracking errors are finite-time convergent. Finally, simulation results are presented to show the effectiveness of the proposed control scheme.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.001 |
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".