Impedance Shaping for Improved Feel in Hydraulic Systems
Bibliographic record
Abstract
Applying haptic control to mobile hydraulic equipment presents a practical yet challenging application. One criticism of newer electro-hydraulic system is a lack of “feel.” To a haptics researcher this sounds like a call for haptic feedback in the human-machine interface. However, for an operator the “feel” of the system likely has more to do with how the actual system responds to forces or higher work port pressures. At some point, the high pressures slow down the system or naturally redirect flow to lower pressure circuits in a hydro-mechanical system. How this is done plays a large part in the “feel” of the system. In this paper, a paradigm is presented that tries to merge these two concepts of “feel.” Instead of trying to make the system transparent, the goal is to make the system react to forces acting on the system then use haptic feedback to help alert the operator to these forces. This is done by shaping this impedance so that the system provides a response or “feel” that is closer to a typical excavator. A haptic interface is used to enhance the haptic feel. Performance is evaluated using data from human-in-the-loop testing.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".