A Comparative Study of Speed Control Strategies for In-Wheel Direct Current Machines in Electric Vehicles: PI, Fuzzy Logic, and Artificial Neural Networks

This paper proposes a comparative study of three speed controllers for DC motor speed in electric vehicles (EVs). The controllers studied are the Proportional Integral Controller (PI), the Fuzzy Logic Controller (FLC), and the Artificial Neural Networks (ANNs). The primary objective of this study is to evaluate the performance, efficiency, and adaptability of these control techniques for speed control of DC motors. Rigorous simulations using MATLAB/Simulink are used to evaluate the accuracy, response time, and overall control effectiveness of each approach. The PI controller, widely used for its simplicity and robustness, is used as the basis for comparison. The FLC shows advantages in effectively controlling the speed of DC motors. The study also explores the potential of ANNs, using their learning and adaptive capabilities, to control DC motors.