Cross-Coupled Contouring Control of Multi-DOF Robotic Manipulator
Bibliographic record
Abstract
Reduction of contour error is a very important issue for high precise contour tracking applications, and many control systems were proposed to deal with contour tracking problems for two/three axial translational motion systems. However, there is no research on cross-coupled contour tracking control for serial multi-DOF robot manipulators. In this paper, the contouring control of multi-DOF serial manipulators is developed for the first time and a new cross-coupled PD (CC-PD) control law is proposed, based on contour errors of the end-effector and tracking errors of the joints. It is a combination of PD control for trajectory tracking at joint level and PD control for contour tracking at the end-effector level. The contour error of the end-effector is transformed to the equivalent tracking errors of the joints using the Jacobian regulation, and the CC-PD control law is implemented in the joint level. Stability analysis of the proposed CC-PD control system is conducted using the Lyapunov method, followed by some simulation studies for linear and nonlinear contour tracking to verify the effectiveness of the proposed CC-PD control 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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".