Computational efficient model predictive current control for interior permanent magnet synchronous motor drives

Abstract

The standard model predictive control (MPC) of three-phase motors requires heavy calculation efforts for evaluating all voltage vectors (VV) in addition to the variable switching frequency, large current harmonics, and torque ripples. To deal with these problems, a computational efficient model predictive current control (MPCC) is proposed for a three-phase interior permanent magnet synchronous motor (IPMSM). Initially, to avoid the protracted enumeration process, the reference voltage vector (RVV) is directly calculated by using the reference current generated by the maximum torque per ampere technique (MTPA), which is an additional control objective. The position of the RVV is utilized to define three candidate voltage vectors to be examined using the cost function, which determines the optimal vector. Secondly, an optimal duty cycle (ODC) is designed to minimize the error between the optimal vector and the reference vector reducing the current ripples. Furthermore, the proposed scheme is compared with the conventional MPCC techniques using MATLAB simulation and hardware in loop (HIL) with TMS320F28335 digital signal processor (DSP) experiments. A comprehensive analysis of the results for different operating conditions shows the effectiveness and robustness of the proposed method.