Adaptive Output Force Tracking Control of Hydraulic Cylinders With Applications to Robot Manipulators
Bibliographic record
Abstract
An adaptive output force control scheme for hydraulic cylinders is proposed by using direct output force measurement through loadcells. Due to the large and somewhat uncertain piston friction force, cylinder chamber pressure control with Coulomb-viscous friction prediction may not be sufficient enough to achieve a precise output force control. In the proposed approach, the output force error resulting from direct measurement is used not only for feedback control, but also to update the parameters of an appropriate friction model which includes the Coulomb-viscous friction force in sliding motion and the output force dependent friction force in presliding motion. The L2 and L∞ stability is guaranteed for both the pressure force error and the output force error. Under bounded desired output force and its derivative, asymptotic stability of both the pressure force error and the output force error is also guaranteed. The experimental results demonstrate that a good pressure force control system does not necessarily guarantee a good output force control, and that adaptive friction compensation is superior to fixed-parameter friction compensation. The output force control transfer functions of a robot joint driven by two hydraulic cylinders in pull–pull configuration are limited by ±1.5dB up to 20Hz, tested in free motion and in rigid constraint. The excellent output force (joint torque) control performance implies the dynamic equivalency between a hydraulic cylinder and an electrically-driven motor within the prespecified bandwidth. This allows to emulate an electrically-driven robot by a hydraulic robot.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".