Effect of Controller in Reducing Steady-State Error due to Flow and Force Disturbances in the Electrohydraulic Actuator System
Bibliographic record
Abstract
This paper pertains to the nonlinear control of a high-precision hydrostatic actuation system known as the Electro- Hydraulic Actuator (EHA). It describes the action of the controller in reducing the steady state error resulting from flow and force disturbances. The EHA uses inner-loop pump velocity feedback to achieve an unprecedented level of accuracy for a hydrostatic system. A published mathematical model of the EHA is reviewed and expanded to produce an equation that predicts the response of the EHA to both desired inputs as well as flow and force disturbances. This equation suggests that the use of a proportional outer-loop controller should result in steady-state error as a result of these disturbances, but that a PI outer-loop controller should eliminate the steady-state error. Experimental results from a prototype of the EHA demonstrate that due to the nonlinear friction present in the actuator, the use of a conventional proportional or PI controller is not sufficient to effectively deal with these disturbances. However, a nonlinear proportional outer-loop controller does result in a substantial performance improvement in regards to disturbance rejection for positional accuracy. Experiments conducted on the prototype using the nonlinear controller reveal that it is capable of a positional accuracy of 1 μm for a load of 20 kg.
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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 | 0.002 |
| 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".