Basic Tutorial on Model Predictive Control for Speed Control of DC-motor

This tutorial paper presents a foundational introduction to Model Predictive Control (MPC) using DC motor speed control as a practical case study. While Proportional-Integral-Derivative (PID) controllers remain widely used in industry due to their simplicity, they often lack the ability to manage system constraints or anticipate future behavior. In contrast, MPC offers a predictive, optimization-based approach that considers such constraints over a finite prediction horizon. To evaluate its effectiveness, MPC is compared with PID across three simulation-based tests: unit step response, input disturbance rejection, and signal tracking. The results show that, within the simulated environment, MPC achieves improved control performance, reducing settling time by 75%, overshoot by 76.6%, and Integral of Absolute Error (IAE) by 49% in step response tests. Under input disturbances and dynamic set-point changes, MPC also achieves lower IAE and faster recovery than PID. Although MPC requires slightly more computational time (0.83 s vs. 0.68 s), this trade-off is manageable in many control applications. Nonetheless, these findings are limited to simulation and do not yet account for real-time implementation challenges, such as tuning complexity, hardware limitations, or model uncertainty. Future work should address these practical concerns to assess MPC’s viability for real-world DC motor systems.