Low Complexity Model Predictive Control—Single Vector-Based Approach

摘要

Finite control set model predictive control (FCS-MPC) is emerging as a powerful control scheme in the control of power converters, because it takes the discrete nature of power converters into account and offers a flexible way to consider various constraints. However, conventional FCS-MPC requires to evaluate a cost function for each discrete switching states, which poses high computational burden. This paper proposes a low-complexity MPC (LC-MPC), which only requires one prediction to find the best voltage vector. The principle of LC-MPC is inherited from prior direct current control (DCC), but has been generalized by identifying its advantages, limitations, and potential application areas. Furthermore, the relationship between LC-MPC and FCS-MPC is studied and it is found that in some cases, the LC-MPC is completely equivalent to FCS-MPC. This paper presents the application example of LC-MPC in power control of three-phase ac/dc converter. To make it a success, the negative conjugate of complex power in synchronous frame is selected as the control variable. Detailed principle of vector selection is introduced and the reason for requiring only one prediction in the proposed LC-MPC is strictly proven using mathematical tools. The proposed LC-MPC is compared with conventional FCS-MPC and its effectiveness is verified by both simulation and experimental results from a two-level ac/dc converter.