Andonovski, G.Leite, D.Precup, R.E.Gomide, F.Pratama, D.Škrjanc, I.2025-12-182025-12-182025Applied Soft Computing, online, 2025; online(114058):1-411568-49461872-9681https://hdl.handle.net/11541.2/44733In an era of increasing system complexity and growing demands for autonomy and efficiency, control systems must continuously adapt to dynamic and uncertain environments. This study presents a comprehensive survey of evolving fuzzy and neuro-fuzzy controllers, with emphasis on data-driven control systems that adapt in real time in both structure and parameters. As the demand for adaptive and flexible control solutions grows alongside the increasing complexity of systems, evolving model-free and model-based fuzzy, neural, and neuro-fuzzy controllers have emerged as robust approaches, allowing models and controllers to integrate new patterns from data streams.enCopyright 2025 the Authors, this is an Open Access article under the Creative Commons license Attribution 4.0 International (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/)Evolving intelligenceIncremental machine learningFuzzy systemsNeural networksAdaptive and real-time controlData-driven controlJournal article10.1016/j.asoc.2025.114058