Autotuning Technique for the Cost Function Weight Factors in Model Predictive Control for Power Electronic Interfaces

Abstract

This paper presents an autotuning technique for the online selection of the cost function weight factors in model predictive control (MPC). The weight factors in the cost function with multiple control objectives directly affect the performance and robustness of the MPC. The proposed method in this paper determines the optimum weight factors of the cost function for each sampling time; the optimization of the weight factors is done based on the prediction of the absolute tracking error of the control objectives and the corresponding constraints. The proposed method eliminates the need of the trial-and-error approach to determine a fixed weight factor in the cost function. The application considered is a capacitor-less static synchronous compensator based on the MPC of a direct matrix converter. This technique compensates lagging power factor loads using inductive energy storage elements instead of electrolytic capacitors. The result demonstrates that the proposed autotuning approach of cost function weights makes the control algorithm robust to parameter variation and other uncertainties in the system. The proposed capacitor-less reactive power compensator based on the autotuned MPC cost function weight factor is verified experimentally.