Controller Design and Stability Analysis of Output Pressure Regulation in Electrohydrostatic Actuators
Bibliographic record
Abstract
In this paper, a robust fixed-gain linear output pressure controller is designed for a double-rod electrohydrostatic actuator using quantitative feedback theory (QFT). First, the family of frequency responses of the system is identified by applying an advanced form of fast Fourier transform on the open-loop input–output experimental data. This approach results in realistic frequency responses of the system, which prevents the generation of unnecessary large QFT templates, and consequently contributes to the design of a low-order QFT controller. The designed controller provides desired transient responses, desired tracking bandwidth, robust stability, and disturbance rejection for the closed-loop system. Experimental results confirm the desired performance met by the QFT controller. Then, the nonlinear stability of the closed-loop system is analyzed considering the friction and leakage, and in the presence of parametric uncertainties. For this analysis, Takagi–Sugeno (T–S) fuzzy modeling and its stability theory are employed. The T–S fuzzy model is derived for the closed-loop system and the stability conditions are presented as linear matrix inequalities (LMIs). LMIs are found feasible and thus the stability of the closed-loop system is proven for a wide range of parametric uncertainties and in the presence of friction and leakages.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".