Study of Takagi‐Sugeno fuzzy‐based terminal‐sliding mode fault‐tolerant control
Bibliographic record
Abstract
This study studies the fault‐tolerant control (FTC) design based on the Takagi‐Sugeno (T‐S) fuzzy system models and terminal‐sliding‐mode control (TSMC). This hybrid scheme can keep the advantages of both methods. By using the T‐S fuzzy models to approximate the original non‐linear system, the online computation burden can be alleviated since most of the T‐S parameters can be offline computed. Moreover, TSMC not only owns the merits, including robustness to uncertainties and/or disturbances, fast response and easy implementation, but also performs better than conventional sliding‐mode control (SMC) since the system states of TSMC will converge in finite time to the control objective point, that is, equivalent point, after the system states intersect sliding surface. Both of the active and passive FTC design schemes are presented. The proposed analytical results are also applied to the FTC for the attitude stabilisation of a spacecraft. Simulation results demonstrate the benefits of the proposed scheme.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".