FUZZY LOGIC CONTROLLER AND ITS APPLICATION IN PROCESS CONTROL

Abstract

Science, and technological advancements have created the revolution in the field of automation. Modern technologies are being used in many areas and It has created a rapid expansion of automation. Fuzzy logic control is the one of the best way to capture automation process of the system. Fuzzy logic, based on fuzzy set theory, is a logical system, which is much closer to human thinking and natural language than traditional logical system. It plays a basic role to capture human thinking and uses linguistic labels of fuzzy sets, fuzzy rules. The fuzzy logic technique provides a direct way to translate qualitative and Imprecise linguistic statements about control procedure and goal for system performance. The methodology of the fuzzy logic is very useful when system knowledge is complex for the analysis by conventional quantitative technique or when the available source of information are interpreted qualitatively, inexactly or uncertainly. In the process control system, where informations are ill-defined and Imprecise fuzzy logic controller gives better performance than other conventional control system. Fuzzy logic controller is a knowledge based system and uses interpolative nature of control rules. The smooth transition of control action is obtained by using these controller. Complex systems which are difficult to control can be effectively controlled lby fuzzy logic control. Nuclear reactors are generally used for power iv generation. The power output depends on the nuclear reaction (fission) and its regulation. The control of power is very difficult due to ill-defined and vague information about reactor core during operation. The fuzzy logic controller implementation in reactor control gives the smooth transitions of regulating rod action so that transients do not occur during operation. It considers all the possibilities and designed by experimental and experience basis. In this dissertation the fuzzy logic control application in this area Is effectively discussed and the control action is derived using the fuzzy inference rule. 6