A novel multiple reference model adaptive control approach for multimodal and dynamic systems
Bibliographic record
Abstract
This paper presents a fuzzy multiple-reference-model generator-based Model Reference Adaptive Control (MRAC) framework for controlling systems that perform a wide range of operating conditions. Following a rule base, the Fuzzy Logic Switching Scheme (FLSS) effectively monitors changes in operating conditions or such drastic changes in plant parameters, and generates a fuzzified reference model output. Then, a single adaptive controller forces the plant output to track the reference, even when plant mode changes. The proposed fuzzy switching Multiple Reference Model Adaptive Controller (MRMAC) is effective as well as feasible for online application, monitoring the plant output at selected control intervals. Unlike static multiple-model algorithms for switching (individual model-based filters do not interact) or switching dynamic algorithms (which are susceptible to numerical overflow), this scheme provides an interactive multiple model generator with soft switching. The strength of the scheme is demonstrated by an application to a theoretical system with disturbed model parameters and for the position tracking of a single-link manipulator. Investigation results show that the proposed scheme performs very positively at different operating modes.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".