Tracking Control of Flexible Ball Screw Drives With Runout Effect and Mass Variation
Bibliographic record
Abstract
Most machine tools rely on precision ball screw drives to accurately position the workpiece relative to the tool. The quality of the machining outcome depends significantly on the tracking performance of the workpiece position over a desired trajectory. This paper addresses the minimization of the tracking error in a ball screw drive system in the presence of dynamic variations. Three sources of dynamic variations are considered: mechanical flexibility, runout of the ball screw shaft, and workpiece mass change during operations. Dynamic variations due to flexibility and runout are related to the workpiece position which is continuously measurable, while the variation caused by the workpiece mass change is uncertain. The ball screw drive system affected by dynamic variations is expressed as an uncertain linear model with time-varying parameters. Based on this model, servo controllers are designed such that their parameters are adjusted in real time by the measurable workpiece position to improve the tracking performance and that their performance is maintained robustly over uncertain mass variation. The importance of taking into account flexibility and runout of the shaft, as well as mass variation, explicitly in controller design is demonstrated through a ball screw drive experimental setup.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".