Design and Stability Analysis of a QFT Pressure Controller of a Hydraulic Actuator Using Takagi-Sugeno Fuzzy Model
Bibliographic record
Abstract
In this paper, a robust, fixed-gain, and linear controller is designed for the output pressure of an electro-hydraulic actuator with parametric uncertainties. Quantitative feedback theory (QFT) is selected as the design technique. The objective is to satisfy specified performance criteria in terms of tracking, stability, and disturbance rejection. To design the QFT controller, the required family of frequency responses is obtained by linearizing the hydraulic nonlinear function around operating points of interest, and constructing an equivalent linear plant set. As a result, the stability of the closed-loop system is guaranteed only around the limited number of operating points, and specified values for system parameters. To overcome this limitation, Takagi-Sugeno (T-S) fuzzy modeling is employed. This way the nonlinear stability of the closed-loop system is investigated and ensured for a continuous range of parametric uncertainties and region of operating points. Having successful results from stability analysis, the QFT controller is applied on the experimental set-up. The experimental results are in accordance with the specified criteria.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".