Active Compliant PID Learning Control for Grinding Robots
Bibliographic record
Abstract
This article proposes a Proportional, Integral, and Derivative (PID) learning controller for rigid robotic disk grinding mechanism. It has been observed that the stiffness of the robotic arm for a grinder has a direct correlation with the sensitivity of the grinding forces. It is also drastically influenced by the end-effector path tracking error resulting in limited accuracy of the robot. The error in robot’s accuracy is also increased by external interferences, such as surface imperfections and voids in the subject material. These errors can be mitigated via efficient feedback. In the proposed methodology, the controller gain is tuned by implementing a learning-based methodology to PID controllers. The learning control for the robotic grinding system helps by progressively decreasing error between the actual grinded paths and required trace. Experimental results demonstrate that as the grinder machines the required path iteratively, its grinding accuracy improves due to the learning algorithm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".