Hybrid actuator for robot manipulators: design, control and performance
Bibliographic record
Abstract
A hybrid actuation method for robotic manipulators is proposed. The actuator employs a hybrid combination of DC servomotors and muscle-like bladder actuators. One DC motor-muscle actuator pair is arranged coantagonistically with an identical DC motor-muscle actuator pair to drive a manipulator joint. Through a suitable control applied to the hybrid actuator, independent control of joint torsional stiffness and joint position is made possible. When air pressure is varied in the muscle actuators, the muscle actuator stiffness and length change. In order to only affect the hybrid actuator stiffness with this pressure change. DC servomotors are used to compensate for muscle actuator length changes, hence joint position is unaffected. High-gain servomotor control ensures a response to disturbances due almost solely to the muscle actuator stiffness characteristics. Dynamic equations of motion are developed for a two-joint manipulator with a hybrid actuator used to drive the final link. A control is formulated for this system to achieve, in addition to independent joint torsional stiffness and joint position control, approximately decoupled and linearized dynamics. A numerical simulation of this two-degree-of-freedom system is presented to verify the performance of the closed-loop system.>
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".