Comparative analysis of tuning a PID controller using intelligent methods

Abstract

A proportional integral derivative (PID) controller is the most commonly used controller in controlling industrial loops due to its simple structure, robust nature and easy implementation. Tuning a PID controller is an important task. The conventional methods for tuning a PID controller have certain limitations. These limitations can be taken care by tuning the PID controller using intelligent techniques. This paper presents the intelligent methods based on fuzzy logic, artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and genetic algorithms (GA) for tuning a PID controller. The controller tuned by the given methods has been used for concentration control of a continuous stirred tank reactor (CSTR). Simulation results reveals that intelligent methods provide better performance than the conventional Zeigler Nichols (ZN) method in terms of various performance specifications.