A SOLUTION FOR NONLINEAR STABILITY ANALYSIS OF QFT CONTROLLERS DESIGNED FOR HYDRAULICALLY ACTUATED SYSTEMS
Bibliographic record
Abstract
Quantitative feedback theory (QFT) is a well-established technique to design robust and linear controllers. However, the important open problem of extending the small signal stability to nonlinear stability verification has remained an ongoing research in the design of QFT controllers. In this paper, we show that Takagi–Sugeno (T–S) fuzzy modeling approach and its stability theory provide a new opportunity to study the nonlinear stability of QFT controllers in fluid power systems. To validate the approach, two case studies are provided first. The first case study establishes the reliability of the approach by confirming the results for a hydraulic system of which nonlinear stability has already been proven. The second case study establishes that using the proposed approach, we can further study and extend the stability region of previously developed hydraulic controllers to include parametric uncertainty. Followed by the successful validation of the effectiveness of our approach through these two case studies, the stability of a QFT position controller, for which the nonlinear stability was never proven, is investigated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".