From Model-Based and Adaptive Control to Evolving Fuzzy Control

TL;DR

This paper reviews the evolution of fuzzy control systems, emphasizing their development, advantages in nonstationary environments, and future challenges like safety and interpretability.

Contribution

It provides a historical overview and highlights recent advances in evolving fuzzy systems for modeling and control.

Findings

Evolving fuzzy systems adapt models incrementally from data streams.

They are effective in nonstationary environments.

Key challenges include safety and interpretability.

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.