20.500.12556/RUL-175596 Advancements in data-driven evolving fuzzy and neuro-fuzzy control a comprehensive survey In 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. Incremental machine learning methods enable control systems to autonomously detect and track new behaviors, improving their effectiveness in time-varying and unknown environments. Based on a rigorous bibliometric analysis using the Web of Science database, 2760 related papers were identified of which 97 were manually selected for detailed review due to their direct relevance to closed-loop evolving fuzzy or neuro-fuzzy control systems. These papers cover a wide range of methods, including basic parameter tuning, adaptive gain scheduling, and structural modifications grounded in constrained optimization and Lyapunov stability analysis. Such advances mark significant progress in the control of unknown, time-varying systems, with the surveyed literature demonstrating promising results in various applications. The abstracted findings reveal an increase in publications since 2013, confirming the relevance of evolving control in engineering. This review provides a comprehensive analysis of methodologies and achievements in the field, highlighting emerging trends, challenges, and research directions within evolving data-driven control. The novelty of this study lies in its focus on the structural evolution of controllers under real-time constraints, consolidating incremental machine learning for partition-based closed-loop architectures. incremental machine learning fuzzy systems neural networks adaptive and real-time control data-driven control evolving intelligence inkrementalno strojno učenje mehki sistemi nevronske mreže adaptivno in sprotno vodenje vodenje na osnovi podatkov true false true Angleški jezik Slovenski jezik Članek v reviji 2025-11-05 10:36:41 2025-11-05 10:36:48 2026-07-07 04:17:34 0000-00-00 00:00:00 2026 0 0 17 str. pt. A, art. 114058 Vol. 186 Jan. 2026 0000-00-00 Zaloznikova Objavljeno apc 0000-00-00 0000-00-00 0000-00-00 681.5 1872-9681 10.1016/j.asoc.2025.114058 254323715 1-s2.0-S1568494625013717-main.pdf 1-s2.0-S1568494625013717-main.pdf 1 DED964BCA5FC59DA658477DC1852B797 31614c156c02cc78f8396cf72c088c00106b173d7fa6753fc9d6def9c1c1d143 113400cb-7911-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=237057 https://www.sciencedirect.com/science/article/pii/S1568494625013717 1 a3dc4b6e-ba2a-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=221374 Fakulteta za elektrotehniko 0 0 0