IEEE Access Special Section Editorial: Advanced Modeling and Control of Complex Mechatronic Systems With Nonlinearity and Uncertainty
Bibliographic record
Abstract
Various complex mechatronic systems are widely applied in industries such as robotics, micro-electro-mechanical systems (MEMS), and motor or hydraulic-driven equipment, and control technology is one of the key issues for mechatronic systems to achieve the desired high performance. However, for those complex mechatronic systems, mechanism nonlinearities and uncertainties (e.g., internal uncertainties, external unstructured environments, and undesired disturbances) are much more obvious and bring significant negative effects. Thus, effective modeling, identification, and dynamic analysis are necessary for complex mechatronic systems with nonlinearity and uncertainty. Model-based advanced control designs, such as adaptive control, robust control, sliding-mode control, backstepping control, and H-infinite control, are the corresponding solutions to improve system performance. Advanced modeling and control is one of the key points in industries for those complex mechatronic systems, where nonlinearity and uncertainty become more and more challenging to pursue higher system performance. The topic of this Special Section in IEEE Access is mainly focused on the categories of control systems, robotics, and automation and also covers other categories such as systems, man, and cybernetics, industrial electronics, and mechanical engineering. We received a total of 104 submissions, and after a rigorous review process, 29 articles have been selected for publication, which are briefly discussed next.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.018 |
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".