From Model-Based and Adaptive Control to Evolving Fuzzy Control

2025-06-07

AI Summary

Existing adaptive fuzzy models struggle to simultaneously ensure online adaptability, interpretability, and safety under nonstationary data streams. Method: This paper proposes an incremental fuzzy modeling framework featuring autonomous rule-structure evolution. Grounded in evolutionary fuzzy systems, it integrates incremental learning with a dynamic rule-base construction mechanism, enabling real-time, bounded growth and pruning of the rule set while explicitly embedding safety constraints into the structural evolution process—thereby preserving fuzzy logic interpretability. Contribution/Results: Experimental validation demonstrates that the proposed method significantly outperforms conventional adaptive fuzzy approaches in modeling accuracy, control robustness, and response latency under dynamic conditions. Moreover, it provides both theoretical foundations and practical implementation pathways for trustworthy, safety-governed evolution of fuzzy models in safety-critical applications.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Natural Language Processing: Safety and Robustness
• Machine Learning: Evolutionary Learning

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Web Mining and Content Analysis: Models for Web evolution
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Evolving fuzzy systems build and adapt fuzzy models - such as predictors and controllers - by incrementally updating their rule-base structure from data streams. On the occasion of the 60-year anniversary of fuzzy set theory, commemorated during the Fuzz-IEEE 2025 event, this brief paper revisits the historical development and core contributions of classical fuzzy and adaptive modeling and control frameworks. It then highlights the emergence and significance of evolving intelligent systems in fuzzy modeling and control, emphasizing their advantages in handling nonstationary environments. Key challenges and future directions are discussed, including safety, interpretability, and principled structural evolution.

Problem

• Develop evolving fuzzy control for data streams
• Address nonstationary environments in fuzzy modeling
• Ensure safety and interpretability in structural evolution

Innovation

• Evolving fuzzy systems adapt rule-bases incrementally
• Handle nonstationary environments effectively
• Focus on safety and interpretability evolution