Designing robust force control of hydraulic actuators despite system and environmental uncertainties
Bibliographic record
Abstract
The article presents the design of a robust force controller for a hydraulic actuator interacting with an uncertain environment via quantitative feedback theory (QFT). After the derivation of a realistic nonlinear differential equation model, a linearized plant transfer function is developed. The effects of nonlinearities are accounted for by describing the linearized model parameters as structured uncertainty. The impact of environmental variability as well as variations in hydraulic component parameters are also included as uncertainty in the model. The QFT design procedure is carried out to design a robust controller that satisfies performance specifications for tracking and disturbance rejection. The designed controller enjoys the simplicity of fixed-gain controllers, is easy to implement, and at the same time is robust to the variation of hydraulic functions as well as environmental stiffness. The controller is implemented on an industrial hydraulic actuator equipped with a low-cost proportional valve. The experimental results show that robust stability against system uncertainties and under varying conditions is achieved and the performance goals are satisfied.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".