Development of a Fuzzy Logic-Based Control System for Process Optimization

Abstract

In modern industrial environments, achieving optimal control of complex and nonlinear processes remains a significant challenge. Conventional control techniques such as proportional–integral–derivative (PID) controllers often struggle to maintain performance in systems with uncertainties, nonlinearities, and time-varying dynamics. This paper presents the development and performance evaluation of a fuzzy logic- based control system for process optimization. The proposed system utilizes fuzzy inference mechanisms to mimic human decision-making and improve control accuracy and adaptability. A microcontroller-based implementation is designed to integrate sensors, fuzzy logic algorithms, and actuators in a closed-loop configuration. The system is evaluated under various operating conditions, and performance metrics such as response time, stability, and error minimization are analyzed. The results demonstrate that the fuzzy logic-based controller outperforms conventional control methods in terms of robustness, flexibility, and efficiency, making it suitable for real-time industrial applications.