Lyapunov stable displacement-mode haptic manipulation of hydraulic actuators: theory and experiment
Bibliographic record
Abstract
In this article, a stable control scheme is designed and experimentally evaluated for haptic-enabled teleoperated control of hydraulic actuators. At the actuator (slave) side, the controller allows the hydraulic actuator to have a stable position tracking. At the master side, the haptic device provides a kind of ‘feel’ of telepresence to the operator by creating a force that acts like a virtual spring, coupling the displacement of the haptic device to the displacement of the hydraulic actuator. In free motion, this virtual spring restricts the operator's hand to move fast when the slave manipulator is behind/ahead in terms of tracking the master manipulator's displacement. On the other hand, when interacting with the environment, the constrained force imposed on the hydraulic actuator is indirectly reflected through this virtual spring force. Extension of Lyapunov's stability theory to non-smooth systems is first employed to prove the stability of the resulting control system. Effectiveness of the controller is then validated via experimental studies. It is shown that the control scheme performs well in terms of both positioning the hydraulic actuator and providing a haptic feel to the operator. The control scheme is easy to implement since very little knowledge about system parameters is needed and the required on-line measurements are actuator's supply and line pressures and displacement.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".