Reduced Computational Burden of Modulated Model-Predictive Control for Synchronous Reluctance Motor Drive Applications

Abstract

This paper introduces a novel geometric approach to significantly reduce the computational burden of modulated predictive controllers while maintaining the same steady-state performance and satisfactory dynamic behavior. The proposed geometric method leverages the symmetric properties of the active vectors with respect to the zero vectors in two-level inverters. In addition, the structure of the controller is designed to include the integral of error terms, ensuring zero steady-state tracking error. Several operating points are considered and compared with respect to standard modulated model-predictive control approaches showing similar steady-state performance with a reduced computational effort (about 50%). This enables a broader spectrum of power electronic systems and applications that can be used with the same steady-state performance of standard modulated model-predictive control (M2PC). In addition, the latter option allows for the application of M2PC with high switching-frequency devices, given that a higher sampling frequency leads to an increased switching frequency. The effectiveness of the proposed approach is demonstrated with a synchronous reluctance motor drive application.