Cross-coupled PID control in position domain for contour tracking
Bibliographic record
Abstract
SUMMARY Accurate contour tracking is one of the main tasks in modern manufacturing processes. By considering coupling effects among multiple axes, this paper proposes a cross-coupled proportional-integral-derivative (PID) control developed in position domain, and the controller is applied to a multi-axis computer numerical control (CNC) machine for contour tracking performance improvement. Stability analysis is conducted for the developed position domain cross-coupled PID control using the Lyapunov method, and guidelines for the selection of control gains are provided. The contour tracking performance are improved compared to an equivalent time domain controller, since the reference axis in position domain control does not contribute any error to the overall contouring error of the system. Simulation results demonstrate the effectiveness of cross-coupled PID position domain control for both linear and circular contour tracking, and prove the robustness of the controller to deal with random disturbances. It also shows that position domain cross-coupled PID control provides better contour tracking performance over position domain PID control and the equivalent time domain PID control.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".