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. • Comprehensive Survey – This paper provides an extensive review of evolving fuzzy and neuro-fuzzy controllers, focusing on real-time data-driven control systems. • Categorization of Approaches – Various evolving control methods are systematically categorized into model-free and model-based frameworks for clearer understanding and application. • Focus on Incremental Learning – The survey highlights the role of incremental machine learning and evolving intelligence in enhancing controller adaptability to dynamic and uncertain environments. • Real-World Applications – Practical applications and case studies of evolving fuzzy and neuro-fuzzy control systems are analyzed, demonstrating their effectiveness across diverse domains. • Emerging Trends and Challenges – The paper discusses future research directions, identifying challenges and opportunities for advancing adaptive control systems in complex environment.