This paper presents a comprehensive comparative study of intelligent control strategies for enhancing the speed regulation of wheeled autonomous robots. A detailed mathematical model of the DC motor commonly used in mobile robotic platforms is developed to accurately capture its dynamic behavior, forming the basis for evaluating four controllers: PID, Fuzzy Logic Control (FLC), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Model Predictive Control (MPC). To ensure a fair comparison, all controllers are implemented within a unified simulation framework and analyzed under identical operating conditions, focusing on overshoot, settling time, transient characteristics, and steady-state performance. The study provides a consistent modeling and evaluation environment, systematic performance assessment of classical, intelligent, and predictive controllers, and evidence-driven interpretation of their practical suitability for robotic applications. The results demonstrate that MPC consistently achieves the most favorable dynamic response with reduced overshoot and faster settling time, outperforming PID, FLC, and ANFIS. These findings offer valuable guidance for selecting effective speed-controlstrategies in wheeled mobile robots and underscore the potential of predictive control as a robust solution for next-generation autonomous systems.
Khan et al. (Thu,) studied this question.