Automatic Structural Identification and Vibration Suppression of Industrial Robots using a Custom Active Damper
Bibliographic record
Abstract
Industrial robots experience low frequency vibrations at the tool induced by rapid movements, base vibrations, and process forces. In precision applications such as robotic machining, tool vibrations can lead to poor surface finish and tolerance violation. Active vibration control can suppress vibrations in real time and increase a robot’s dynamic stiffness provided the structural modes of the robot are accurately known. This paper presents a custom active damper that is capable of both automatic frequency response function (FRF) measurement and active vibration control. The active damper, which consists of a linear voice coil actuator, moving mass, and on-board accelerometer, excites the structural modes of the robot by applying a sine sweep force input. A dynamic model is derived from the FRF, which allows for efficient tuning of the direct velocity feedback controller within a simulation environment. The performance of the active damper is experimentally validated on a Staubli RX90CR industrial robot. Experimental results show a significant reduction of the dominant structural mode subject to hammer tests and base vibrations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 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".