Switching Strategy for Direct Model Predictive Control in Power Converter and Drive Applications with high Switching Frequency

Model Predictive Control (MPC) includes a mathematical plant model. Based on that model, optimal actuating variables for future timesteps are determined in every sampling step. Thus the MPC exhibits a better reference response compared to conventional control. The problem with MPC is the high computational cost and the associated long control cycle time. Thus MPC is unattractive for processes with small time constants as they are common in power converter and drive control systems. In this paper a Direct Model Predictive Control method (DMPC) for nonlinear systems with inherent output saturation is presented. In contrast to other Direct-MPC approaches, a more flexible gate-signal generation method which enables switching during the sampling period is utilized. In addition the switching frequency can be increased while maintaining the same controller cycle time. This results in a reduction of the current ripple. Since this approach is based on a computational efficient optimization algorithm, it provides real-time capability for online-MPC even with process time constants in the millisecond range enabling the use of MPC for control of permanent magnet synchronous motors with interior magnets (IPMSM).