Automated Identification of a Mechatronic System Model Using Genetic Programming and Bond Graphs
Bibliographic record
Abstract
Modeling, identification (experimental modeling), and design of dynamic systems and the associated problem of controller design are common problems in the field of mechatronics. A typical mechatronic problem entails finding the best topology as well as parameter values of the desired solution. In view of dynamic interactions in a mechatronic system, which involves more than one domain, it is desirable to use concurrent and integrated methodology in the solution. The powerful search ability of genetic programming (GP) along with the domain independence and the open architecture of bond graph (BG) modeling can be integrated to develop an evolutionary mechatronic tool for identification of a complex mechatronic system. This paper extends this integrated approach to nonlinear mechatronic problems and develops a software tool for this purpose. It is illustrated how the developed technique and the corresponding software tool can be used in the automated synthesis and identification of a nonlinear mechatronic system. The performance of the software tool is validated by applying it to a nonlinear electrohydraulic manipulator, which falls into the class of multidomain systems. The results obtained from the application are quite encouraging, and form a rationale for the extension of the tool for concurrent and optimal design of mechatronic systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".