FUZZY CONTROL SYSTEMS FOR SMART AND AUTONOMOUS SYSTEMS

Abstract

Fuzzy control systems (FCS) constitute an intelligent control paradigm that models human reasoning and decision-making to manage complex, nonlinear, and uncertain systems. Rooted in fuzzy set theory introduced by Lotfi A. Zadeh, FCS employ linguistic variables, membership functions, and rule-based inference to translate expert knowledge into effective control actions without requiring precise mathematical models. This capability makes fuzzy controllers particularly suitable for systems characterized by ambiguity, imprecision, or time-varying dynamics. Over the years, FCS have demonstrated robust performance, adaptability, and ease of implementation across diverse domains, including industrial process control, robotics, automotive systems, power systems, and smart and autonomous technologies. Recent advancements integrate fuzzy logic with optimization methods, neural networks, and evolutionary algorithms to enhance learning, tuning, and scalability. Overall, fuzzy control systems offer a flexible and interpretable framework that bridges human expertise and automated control, contributing significantly to the development of reliable and efficient smart and autonomous systems.

Unlike conventional control methods that rely on precise mathematical models, fuzzy logic enables approximate reasoning similar to human decision-making. This paper reviews the fundamental concepts, architectures, recent advancements, and applications of fuzzy control in smart systems such as autonomous vehicles, robotics, smart grids, healthcare systems, and industrial automation. Hybrid intelligent systems combining fuzzy logic with neural networks, evolutionary algorithms, and machine learning techniques are also discussed. Finally, challenges and future research directions are presented.