Design and Control of a Hydraulic Simulator for a Flexible-Joint Robot
Bibliographic record
Abstract
For the first time, a novel experimental hydraulic system that simulates joint flexibility of a single-rigid-link flexible-joint robot manipulator, with the ability of changing the joint flexibility's parameters, was designed and implemented in this study. Such a system could facilitate future control studies of robot manipulators by reducing investigation time and implementation cost of research. It could also be used to test the performance of different strategies to control the movement of flexible-joint manipulators. A hydraulic rotary servo motor was used to simulate the action of a flexible-joint robot manipulator, which was a challenging task, since the control of angular acceleration was required. In this study, a single-rigid-link elastic-joint robot manipulator was mathematically modeled and implemented in which joint flexibility parameters such as stiffness and damping could be easily changed. This simulation is referred to as a 'function generator' to drive a hydraulic robot manipulator. In this study the desired angular acceleration of the manipulator was used as the input to the hydraulic rotary motor and the objective was to make the hydraulic system follow the desired acceleration in the frequency range specified. A hydraulic actuator robot was built and tested. The results indicated that if the input signal had a frequency in the range of 5–15 Hz and damping ratio of 0.1 (typical values for flexible joints), the experimental setup was able to reproduce the input signal with acceptable accuracy. Owing to the inherent noise associated with the measurement of acceleration and some severe nonlinearities in the rotary motor, control of the experimental test system using classical methods was a challenging task that had not been anticipated.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".