A Structured Linear Quadratic Gaussian Based Control Design Algorithm for Machine Tool Controllers Including Both Feed Drive and Process Dynamics
Bibliographic record
Abstract
A new extension of the stochastic linear quadratic Gaussian (LQG) regulator problem is developed and used for the design of new suboptimal cross-coupling controllers for machine tool drives. This new extension allowed us to combine both the drive and the cutting dynamics into a unified model driven by the static and the dynamic portions of the cutting force. The dynamic portion of the cutting force is considered as a stochastic random process in end milling contouring processes. The outputs of the axes are corrected by the cutting tool deflections which result from the cutting force-workpiece resistance interactive dynamics. Most importantly, the LQG extension developed here is directly applicable to the design and optimization of centralized, decentralized, and hierarchical machine tool controllers that have previously appeared in the literature. This is possible because our extension allows the assignment of a different control structure for each control input even if more than one control input are contributing to the same axis. Furthermore, the method admits each controller to function in any chosen subset of the available measurements. Thus, it provides us with a powerful means for designing any of the above-mentioned controllers using the same approach. The results of our suboptimal cross-coupling controllers were magnificent when compared to the commercially available positioning controllers.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".