Adaptive Fractional-Order Fault-Tolerant Tracking Control for UAV Based on High-Gain Observer
Bibliographic record
Abstract
This paper is concerned with the fractional-order fault-tolerant tracking control design for unmanned aerial vehicle (UAV) in the presence of external disturbance and actuator fault. Based on the functional decomposition, the dynamics of UAV is divided into velocity subsystem and altitude subsystem. Altitude, flight path angle, pitch angle and pitch rate are involved in the altitude subsystem. By using an adaptive mechanism, the fractional derivative of uncertainty including external disturbance and actuator fault is estimated. Moreover, in order to eliminate the problem of explosion of complexity in back-stepping approach, the high-gain observer is utilized to estimate the derivatives of virtual control signal. Furthermore, by using a fractional-order sliding surface involved with pitch dynamics, an adaptive fractional-order fault-tolerant control scheme is proposed for UAV. It is proved that all signals of the closed-loop system are bounded and the tracking error can converge to a small region containing zero via the Lyapunov analysis. Simulation results show that the proposed controller could achieve good tracking performance in the presence of actuator fault and external disturbance.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".