Performance Evaluation of Different Conventional and Intelligent Controllers for Temperature Control of Shell and Tube Heat Exchanger System

Abstract

In any of the control application, controller design is the most important part. There are different types of controller architectures available in control literature. The controller can be conventional in nature or can be intelligent in nature. The conventional controller doesn’t posses the human intelligence; where in the intelligent controller human intelligence is embed with the help of certain soft computing algorithms. After the design of controller is performed, the performance evaluation part comes in to light. The designed controller has to give optimal control results irrespective of every situation like plant and equipment non linearity, equipment saturation. This dissertation looks in to performance evaluation of different conventional and intelligent controllers implemented with a clear objective to control the outlet fluid temperature of shell and tube heat exchanger system. First of all mathematical modeling of the process is performed using experimental plant data. After the mathematical modeling the control objective is set and different kind of controllers are designed to meet the control objective. Feedback controller, feedback plus feed forward controller are implemented to meet the control objective, but due to their inherent disadvantages and more tuning parameters, these controllers were unable to give satisfactory results. So, a model based controller is designed which has only one tuning parameter as compared to three tuning parameters of PID controller. The model based controller gives a satisfactory result. But to embed some kind of intelligence in the controller, fuzzy logic based controller is designed. The fuzzy logic based controller meets the control objective. Comparative analyses of performance evaluation of all controllers are performed. During the design of fuzzy based hybrid controller, the designer meets two key design challenges namely, optimization of existing fuzzy rule base and identification, estimation of new membership function or optimization of existing membership function. These issues play a vital role in controller design in real time. In real time controller hardware design there is memory and computational power constraints, so a designer needs to optimize these two design aspects. This dissertation also looks in to these key design challenges. For optimization of existing mamdani based fuzzy rule base, a genetic algorithm approach is used and for identification and estimation of fuzzy membership function, a neural network based approach is used.