COMPARATIVE STUDY OF CLASSICAL AND SOFT COMPUTING INTELLIGENT CONTROLLERS FOR THE SPEED CONTROL OF A SEPARATELY EXCITED DC MOTOR

Abstract

This paper presents a comparative study of four different controllers, namely, the Proportional Integral (PI) Controller, Fuzzy Logic Controller (FLC), Artificial Neural Network (ANN) Based Controller, and the Adaptive Neuro Fuzzy Inference System (ANFIS) Based Controller, for the speed control of a Separately Excited DC Motor. The study aims to find the best controller for controlling the speed of the DC motor, which is essential to sustain the desired speed trajectory during the operational process. A transient analysis was carried out on individual controllers using a speed reference of 1600 rpm to 2200 rpm, and it was observed that the ANFIS controller demonstrated a higher level of performance in tracking the input reference with an average percentage overshoot of 18.25%, a settling time of 1.446 seconds, and a steady-state error of 0.1%. The results of the study will be useful for industrial processes that require the operation of the DC motor at a desired speed, depending on the load.