A Fractional Adaptive Type-2 Fuzzy Structural Control System: Theorical/Experimental Study
Bibliographic record
Abstract
<title>Abstract</title> A new modified sliding mode control (SMC) method based on type-2 fuzzy neural networks (T2FNNs) is introduced for the active mass damper (AMD) systems. An adaptive T2FNN is used in the switching part, and another adaptive T2FNN is used to estimate the uncertainty of the AMD system. T2FNNs are adopted to predict the system's uncertainties and establish a dynamic model of the AMD system, independent of the mathematical model. The chattering phenomenon is also taken into account and analyzed. The stability is studied by using the Lyapunov approach to derive training rules for both T2FNNs in dynamic modeling and switching part. Numerical simulation and experimental verifications confirm the feasibility and effectiveness of the designed T2FNN based SMC. The results reveal that the designed controller outperforms the conventional controllers in reducing the vibration peak and reducing the root mean square (RMS) value of structural displacement and acceleration. It also exhibits good robustness against external disturbances and structural dynamic perturbations. The suggested algorithm combines the advantages of adaptive control and fuzzy logic, addressing the issue of chattering in control and overcoming the lack of self-learning capability in conventional SMCs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".