Analysis of Fuzzy PID and Immune PID Controller for Three Tank Liquid Level Control

Abstract

Biological immune system (BIS) is a special type of control system that has strong robustness and self-adaptability. This thesis report proposes an artificial immune system algorithm to develop an immune controller. The idea of immune controller is adopted and derived from biological vertebrate immune system, mimicking and imitating of biological immune system which is better known as the artificial immune system . This thesis show how proposes to apply and implement the algorithm of the artificial immune system (AIS) to develop an immune controller (IC) for three tank level control. There are various models of artificial immune controller (AIC). The most suitable for their particular application is selected. The selected artificial immune controller has the resemblance of a PID controller. The immune controller enhances the performance and stability of the system. The approach is to prove that an immune controller using artificial immune system algorithm can be used as a controller to obtain steady state output response. In industrial control systems the liquid level is carrying its significance as the control action for level control in tanks containing different chemicals or mixtures is essential for further control linking set points. The three level control models are considered in our thesis work. The conventional control algorithms are difficult to reach required control quality with more strict restriction on overshoot. Designed a parameter self-tuning PID-controller based on fuzzy control, which can adjust PID-parameters according to error and change in error. Biological immune system is a control system that has strong robusticity and self-adaptability in complex disturbance and indeterminacy environments. The artificial intelligence technique of fuzzy logic and immune controller is adopted for more reliable and precise control action which incorporate the uncertain factors also. In this thesis the comparison of the conventional model, fuzzy model and immune feedback mechanism has been analyzed.