Differential Flatness-Based Model Predictive Speed Control for Direct Current Motor–Gearhead Drive Systems

This paper introduces a new speed control framework design for direct current electric motors coupled with a gearhead. The proposed framework suitably integrates differential flatness, model predictive control, and Kalman filter theory for efficient speed tracking in disturbed scenarios. The performance of the proposed control framework is compared against a flat feed-forward proportional–integral–derivative controller, an extended state offset-free model predictive control, and a linear quadratic integral controller. The results show improved tracking performance with reductions of up to 60% in NRMSE and 50% in ITAE compared to the baseline controllers while maintaining a comparable and bounded control effort due to the constraints enforced in the framework formulation. These results show that the proposed approach effectively captures the dominant disturbance behaviour under varying operating conditions. Thus, it is demonstrated that the flat system formulation allows for proper speed control, providing practical insights for advanced motor control in industrial automation, robotics, and electric mobility applications.