PRECISION CONTOUR TRACKING USING FEEDBACK-FEEDFORWARD INTEGRATED CONTROL FOR A 2-DOF MANIPULATION SYSTEM
Bibliographic record
Abstract
This paper presents a novel approach for precision contour tracking through combining feedback PID and feedforward position domain ILC (PDILC) control for a multi-axis manipulation system.Traditional control approaches in time domain suffer from poor synchronization of relevant motion axes and result in restriction for contour tracking tasks.In the proposed PID & PDILC design, a 2-DOF system is treated as a master-slave cooperative motion system.The position information of the master motion axis is integrated into the PDILC controller of the slave motion axes, which makes the PDILC learn from contour errors instead of individual axis errors.The selection and tuning of parameters for the PID and PDILC were conducted based on the computation in a lifted matrix format of the stability and convergence conditions.The performance of the PID & PDILC controller was evaluated by comparisons with PID and cross-coupled ILC controller through experiments on a multi-axis precise positioning stage.The proposed PID & PDILC design enhances the precision contour tracking of the testbed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".