Passive and active nonlinear fault-tolerant control of a quadrotor unmanned aerial vehicle based on the sliding mode control technique
Bibliographic record
Abstract
In this paper, an augmented sliding mode-based fault-tolerant control is designed theoretically, implemented practically and tested experimentally in a quadrotor unmanned aerial vehicle testbed under propeller damage and actuator fault conditions for tracking control. In view of the significant feature of robustness inherent to the sliding mode control technique, the developed sliding mode-based fault-tolerant control strategies have been designed and implemented in the two currently and widely used types of fault-tolerant control strategy, i.e. passive and active, with the intention to investigate and compare the advantages, disadvantages and application considerations and limitations of these two different fault-tolerant control strategies in the tracking control problem of a quadrotor unmanned aerial vehicle application. Therefore, these two types of controller have been carried out in both theory and practice with and without the presence of faults. Both theoretical and experimental analyses demonstrated the effectiveness of the two sliding mode-based fault-tolerant control strategies in the application to the quadrotor unmanned aerial vehicle under a small level of actuator faults or damage. Detailed comparisons are also provided in the paper to demonstrate the capabilities, advantages and disadvantages of the two types of fault-tolerant tracking controller under different flight conditions in the presence of actuator faults and propeller damage.
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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".