INTELLIGENT CONTROL OF WECS SUBJECT TO PARAMETER UNCERTAINTIES, ACTUATOR AND SENSOR FAULTS
Bibliographic record
Abstract
This paper proposes the robust fuzzy fault tolerant control (FTC) problem for the fuzzy Takagi-Sugeno (TS) systems with sensor faults and actuator faults in the presence of parameter uncertainties. The TS fuzzy model is first employed to approximate a nonlinear uncertain system, and then the fuzzy sensor fault, fuzzy actuator fault model and the fuzzy state observer are developed. Sufficient stabilization conditions of the fuzzy FTC systems are given, which are formulated in terms of linear matrix inequalities (LMIs). The LMIs can be efficiently solved using the convex optimization techniques. Finally, fuzzy observer based on fuzzy fault tolerant controller is developed. It is proved that the fuzzy control system is reliable in the sense of that the closed-loop system is asymptotic stability and the all sensor and actuator components can operate well in the presence of some component failures. The control scheme is tested for real profiles of wind speed pattern and provides satisfactory results
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".