From Model-Based and Adaptive Control to Evolving Fuzzy Control

--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. Research in fuzzy modeling, control, and applications has grown rapidly since Zadeh's seminal work in 1965 [1], evolving into a vast and multifaceted field.