Fractional-Order Control of Hydraulically Powered Actuators: Controller Design and Experimental Validation
Bibliographic record
Abstract
This paper presents a model-free design procedure for the position control of hydraulic actuators. A fractional-order PID (FOPID) controller is designed by employing the Oustaloup recursive method. The controller parameters are tuned experimentally based on the iterative feedback tuning (IFT) technique. The IFT optimizes an objective function using the experimental data taken from an instrumented valve-controlled hydraulic actuator test rig. The objective function, to be minimized, includes both tracking and robust stability criteria. The efficacy of the proposed controller is examined by comparing the experimental results with those from a quantitative feedback theory (QFT) based controller. Although the QFT controller gives rise to good tracking responses, the comparisons show that the FOPID controller results in better settling time in tracking the desired response than the QFT controller. Moreover, it is much less sensitive to the effect of the hunting phenomenon, originating from the dry friction, than the QFT controller. The robustness of the FOPID controller against system uncertainties including the external load, the inertia, and friction is also demonstrated.
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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) | 0.000 | 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 | 0.000 |
| 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".