FUZZY CONTROL SYSTEMS APPLIED TO INDUSTRIAL AUTOMATION: A CASE STUDY IN PRODUCTION PROCESSES

Fuzzy logic control has gained widespread attention in industrial automation due to its ability to handle nonlinearities and uncertainties in complex production environments. Traditional control methods, such as Proportional-Integral-Derivative (PID) controllers, often struggle with dynamic and highly variable processes. In contrast, fuzzy control systems leverage human-like reasoning and linguistic rules to optimize operational efficiency, adaptability, and robustness in manufacturing. This paper explores the implementation of fuzzy control systems in industrial automation, focusing on regulating critical variables such as temperature, speed, and pressure. A comprehensive literature review highlights recent advancements in fuzzy-based control, demonstrating their superiority over conventional methods in maintaining process stability and reducing operational costs. The study also presents real-world case studies illustrating how fuzzy logic enhances precision in industrial furnaces, conveyor belt systems, and hydraulic pressure control. Additionally, the integration of fuzzy logic with artificial intelligence and machine learning is examined as a pathway to further improve automation efficiency and predictive maintenance strategies. The findings suggest that fuzzy controllers offer significant advantages, including reduced energy consumption, improved product quality, and extended equipment lifespan. However, challenges such as expert knowledge requirements and computational complexity remain areas for future development. This research emphasizes the growing potential of fuzzy control systems in optimizing industrial automation and highlights the need for continued advancements to fully harness their capabilities in diverse manufacturing environments.