Fuzzy adaptive fault-tolerant control for quadrotor helicopter
Bibliographic record
Abstract
This paper proposes a fuzzy adaptive sliding mode control for accommodating large actuator fault and maintaining system stability. Boundary layer is employed to smooth control discontinuity and eliminate control chattering. However, if the thickness of boundary layer is chosen too big, the tracking accuracy will be degraded. Whereas, if the boundary layer thickness is too small, control chattering will be stimulated and system stability will also be affected. With this consideration, a fuzzy logic based boundary layer is designed together with the proposed adaptive control scheme to maintain system stability. The on-line adaptive control scheme can seamlessly change control gains to make a compensation in the presence of actuator fault. When there is a trend to overestimate the control gain, the fuzzy boundary layer will be triggered to enlarge boundary layer thickness to ensure system stability. In such a way, larger actuator fault can be accommodated within the proposed control scheme. The effectiveness of the proposed control strategy is validated through a simulation based on a quadrotor helicopter under two different fault scenarios.
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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".