Fuzzy logic and neural network control of industrial thermal systems

The objective of the thesis was to develop an intelligent multistage temperature controller using Artificial Intelligence techniques and prove that the developed controller is much superior in design than the present industry standard PID controller.;The AI controllers were developed using Fuzzy logic and Neural Networks. The Fuzzy logic controller was developed using fuzzy sets and an expert's rules for the process of temperature control. The Neural Network was trained by data derived using a novel technique. An estimator was also developed using Fuzzy logic which controlled the process in cases of sensor failure. A PID controller was included in the simulation to compare the results.;From the simulation results obtained, it was concluded that the Fuzzy logic controller was better in performance, accuracy, and stability for the control of non-linear thermal processes than the PID or the neural network controller.