A Novel Position Domain Controller For Contour Tracking Performance Improvement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A common problem with modern manufacturing processes that utilize high feed-rate machining is how to accurately track a given contour for the tool center point (TCP) of a system. Various methods have been developed to increase axial tracking performance and contouring performance of computerized numerical control (CNC) machines. These include: high gain feedback controllers, feedforward controllers, zero phase error tracking controllers (ZPETC), cross-coupled control (CCC), and iterative learning control to mention a few. The common factor amongst these methods is that they are all based in time domain. This thesis will propose a new control law based in position domain applied to contour tracking control of a CNC machine. The goal of this developed controller is to improve the overall tracking and contouring performance of a CNC system. The idea behind a position domain control involves transforming the dynamics of a system from time domain into position domain through a one-to-one mapping. In the position domain system control, the motion of one of the axis is used as an independent reference by sampling equidistantly to control the remaining axes according to the contouring requirements. The overall contour error in a position domain controller should be lower relative to an equivalent time domain controller since there will be a zero tracking error from the reference motion. The stability of the proposed position domain control is proven through the Lyapunov method. Simulations with linear and nonlinear TCP contours using the proposed position domain controller and an equivalent time domain controller indicate that the proposed position domain control can improve tracking and contouring performance. In addition, a position domain controller with cross-coupled control was also proposed to further improve contour performance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it