Efficiency of MPC Framework Cast to a Linear Programming Problem for a Servo Drive with Model Uncertainty

Thepaper presents an efficient model predictive control framework formulated as a linear programming problem to control a servo drive with model uncertainty considerations from the viewpoint of the control performance. The model predictive framework is used to adopt $L_1$-type cost functions using absolute tracking errors, providing computational efficiency and enabling real-time implementation. A key contribution is the deployment of this approach on real hardware in a hardware-in-the-loop setting, supported by fully open-source code for Simulink Coder and C environments, verifying the solution scheme in real time. Experimental validation on a servo drive demonstrates the system’s tolerance for parameter uncertainties with slight performance degradation, resulting in an up to 18% increase in the considered control quality measure, between nominal parameters’ values and the worst configuration. The proposed linear programming approach enables constraint handling imposed on control signals and supports the arbitrary choice of prediction horizons and sampling intervals. The paper also includes a comprehensive derivation of the control law, controller implementation details, and stepwise experimental results showcasing the impact of uncertainties on control performance. This work and the attached code enable the authors to easily reproduce the proposed approach and extend it in their applications.