Fast and Robust Model Free Predictive Current Control for SynREL Motor Drives

Abstract

Model Predictive Control scheme performances are strongly influenced by an accurate knowledge of the plant model. A Model-Free predictive control approach permits to overcome all the approximations due to parameters variations or mismatches, model non linearities or inadequacies. A Finite-Set Model-Free Current Predictive Control is thus proposed in this paper. The current variations predictions related to the eight feeding voltages are performed by means of the previous measurements stored into look-up tables. To keep the current variations information up to date, the three current reactions related to the three most recent feeding voltages are combined together to reconstruct all the others. The reconstruction is performed by taking advantage of the relationships between three different state voltage vectors. In particular, 210 possible combination of three state voltage vectors can be found, but they can be gathered together in six different groups. A novelty introduced in this work is a light and computationally fast algorithm for the current variation reconstruction based on the state voltage vectors group identification. Finally, the current reconstruction for the prediction at future steps is thoroughly analysed. A compensation of the motor rotation effect on the input voltages $dq$ projections is proposed, too.