Development of Fuzzy Logic Controllers for Non-Linear Industrial Processes

This thesis work outlines the design and simulation of a fuzzy logic control system fortemperature or pressure regulation in automated industrial processes. Although conventional Proportional-Integral-Derivative (PID) controllers are ubiquitous, they lose their optimal performance in nonlinear and dynamic processes. Fuzzy logic controllers (FLCs), on the other hand, provide an intelligent and adaptive solution by simulating human decision-making using rule-based inference and linguistic variables. A simulation model was developed usingMATLAB/Simulink to compare the performance of the FLC with that of a conventional PID controller in regulating the temperature or pressure in a chemical reactor The fuzzy controller used Gaussian membership functions and a predefined rule base, allowing real-time decision-making based on both the error and rate of change. Simulation results verified that FLCs were superior to PID controllers in stability, response time, adaptability, and robustness underdynamic operating conditions. Additionally, the study highlighted the modularity, computational efficiency, and scalability of FLCs, thereby justifying their practical applicability in industrial automation. The project concludes by recommending the integration of FLCs into control systems to promote efficiency and flexibility, while outlining potential areas of research in hardware implementation and hybrid intelligent systems aimed at improving process control