Fault-tolerant control of a quadrotor despite the complete rotor failurevia adaptive Lyapunov-based control
Bibliographic record
Abstract
In this paper, an efficient method is proposed for the position and altitude tracking control of a quadrotor UAV through a nonlinear dynamic model in case of failure of one or two quadrotor rotors and in the presence of parametric uncertainties.In fact, the system continues its tasks correctly even if one or two rotors of the quadrotor stop working.The proposed method is a combination of the Lyapunov stability theory and the neural network adaptive scheme, in which Lyapunov-based controller was designed for subsystems separately, and their coefficients adaptively tuned by the neural network method.Further, the performances of the proposed control method were evaluated.The simulated results demonstrated that the proposed controller exhibits desirable transient behavior and performance stability, is not sensitive to parameter variations, and has remarkable stability and performance robustness despite the complete rotor failure.Hence, for operational purposes where the stability and continuation of the mission in case of failure of the rotors are of vital importance, using an adaptive Lyapunov-based control approach is recommended.
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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) | 0.001 | 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 | 0.000 | 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".