Convex integrated design (CID) method and its application to the design of a linear positioning system
Bibliographic record
Abstract
In this paper, a methodology is presented to solve an integrated mechanical structure and control system design problem, with a set of n prespecified closed-loop performance specifications. Utilizing the convex integrated design (CID) method proposed here, the transfer matrix of the closed-loop system is first determined such that the set of n conflicting closed-loop performance specifications is simultaneously satisfied. However, the mechanical structure parameters and the control system gain parameter choices that comprise the closed-loop system transfer matrix are not uniquely determined. While arbitrary choices of these parameters could be made, the authors propose an approach to determine these design parameters by solving an equality-constrained optimization problem. The merit functions to the optimization problem are the closed-loop performance criteria. With this approach, the mechanical structure parameters, the controller structure and the control gains, are simultaneously determined and the closed-loop system performance is further improved beyond that required by the set of n closed-loop performance specifications. This method is demonstrated with a four-specification linear positioning system design. Experimental results verify the effectiveness of this method.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".