Low Complexity Model Predictive Control—Single Vector-Based Approach

Abstract

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.Index Terms-AC/DC converter, low complexity, model predictive control (MPC), power control.

Direct Power Control of a Pulse Width Modulation Rectifier Using Space Vector Modulation Under Unbalanced Grid Voltages

“…However, table-based DPC produces large power ripples and the switching frequency is variable, which is mainly caused by the use of heuristic switching table and hysteresis comparators. To improve the steady state performance, predictive control methods have been introduced to replace the switching table to achieve more accurate and effective vector selection [10]- [13]. However, they are relatively complicated and relies on the accuracy of system parameters.…”

Prediction Error Analysis of Finite-Control-Set Model Predictive Current Control for IPMSMs

“…It is generally expressed by including the errors between prediction values of controlled variables and their reference values. There are several ways to calculate the errors, such as the absolute error, square of error, or integration of error [29]. Taking into account the tradeoff between simplicity and evaluation performance, the squared form is often utilized for FCS-MPCC, which is written as follow: (2) and (3), and the prediction results are then evaluated using the cost function given in Equation (4).…”

Finite‐control‐set model predictive control for eight‐switch three‐phase NPC converters

“…In recent years, several simplified control methods have been proposed to reduce the computational burden in FCS-MPC [20][21][22][23][24][25]. In [20], in order to reduce the execution time, a simplified FCS-MPC method was proposed by using equivalent coordinate transformation and specialized sector distribution method.…”