Fuzzy logic‐enhanced sliding mode control of Belousov–Zhabotinsky reaction dynamics
Bibliographic record
Abstract
Abstract This study introduces two novel strategies for regulating the chaotic dynamics of the Belousov–Zhabotinsky (BZ) reaction: a smoothed sliding mode controller (SMC‐Proposed), designed to reduce chattering while preserving robustness, and an adaptive fuzzy sliding mode controller (SMC‐Fuzzy), applied to the BZ system for the first time. These approaches are compared against a classical sign‐based sliding mode controller (SMC‐sign) in terms of tracking accuracy, convergence speed, and chattering suppression. Simulation results show that while SMC‐sign achieves the lowest tracking error (RMSE = 0.00001), it produces severe chattering (973.4 Hz). In contrast, the SMC‐Fuzzy controller reduces chattering to 79.2 Hz, with good accuracy (RMSE = 0.00107) and faster stabilization. The SMC‐Proposed model offers a balanced trade‐off, achieving moderate accuracy while significantly reducing high‐frequency energy without relying on fuzzy logic. Frequency‐domain analysis using power spectral density (PSD) confirms the chattering suppression capability of both proposed methods. These findings highlight the practical advantages of the SMC‐Fuzzy and smoothed SMC controllers for robust and efficient control of chaotic chemical 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".