Fault-Tolerant Actuating Pressure Controller Design for an Electrohydrostatic Actuator Experiencing a Leaky Piston Seal
Bibliographic record
Abstract
Electrohydrostatic actuators (EHAs), as a class of pump-controlled hydraulic actuators, are known for energy efficiency and easy maintainability. Thus, they can be widely used in the situations where actuating pressure/force control of hydraulic actuators is essential. Examples are automotive active suspension, deep-drawing press, molding machine, and vibration isolation. However, a leaky piston seal in an EHA system can be especially problematic as it is not visually detectable, but causes internal leakage flowing across actuator chambers impairing the performance. This paper employs quantitative feedback theory (QFT) to design a robust fixed-gain linear actuating pressure controller that is tolerant to actuator internal leakage. Since QFT captures uncertainties by templates, representing frequency responses of the plant on Nichols chart, the first step, to design a QFT controller, is to establish plant templates. In doing so, a set of offline parametric system identifications are implemented, and the family of identified models, providing frequency responses, are used to design the QFT fault-tolerant controller. The controller also satisfies the prescribed design tolerances on tracking, stability and sensitivity (disturbance rejection at plant output) under different conditions, including various levels of actuator internal leakage, environmental stiffnesses, and load masses. The ability of the controller to maintain actuating pressure within the acceptable response envelope is demonstrated in experiments. The experimental results show that the system specifications are satisfied despite internal leakage up to 12 L/min.
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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 | 0.000 |
| 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".