Robustness Analysis of Long-Horizon Direct Model Predictive Control: Induction Motor Drives

Model predictive control (MPC) requires an accurate system model to achieve favorable performance. Thus, in presence of disturbances, model uncertainties and mismatches, MPC needs tools that provide high degree of robustness to them. Since MPC is, essentially, a proportional control technique, an effective method to deal with the aforementioned issues is the addition of an integrating element to the control scheme. This paper presents a prediction model that introduces an integrator tothe control strategy without increasing the size of the optimization problem. To examine its effectiveness, the sensitivity of the classical and the proposed MPC to parameter deviations are discussed and analyzed, considering a wide range of switching frequencies as well as prediction horizon lengths. The robustness examination is performed based on an industrial case study, namely a medium voltage induction motor drive. acceptedVersion