Sequential Model Predictive Control of Three-Phase Direct Matrix Converter

Abstract

The matrix converter (MC) is a promising converter that performs the direct AC-to-AC conversion. Model predictive control (MPC) is a simple and powerful tool for power electronic converters, including the MC. However, weighting factor design and heavy computational burden impose significant challenges for this control strategy. This paper investigates the generalized sequential MPC (SMPC) for a three-phase direct MC. In this control strategy, each control objective has an individual cost function and these cost functions are evaluated sequentially based on priority. The complex weighting factor design process is not required. Compared with the standard MPC, the computation burden is reduced because only the pre-selected switch states are evaluated in the second and subsequent sequential cost functions. In addition, the prediction model computation for the following cost functions is also reduced. Specifying the priority for control objectives can be achieved. A comparative study with traditional MPC is carried out both in simulation and an experiment. Comparable control performance to the traditional MPC is achieved. This controller is suitable for the MC because of the reduced computational burden. Simulation and experimental results verify the effectiveness of the proposed strategy.

Challenges and Design Requirements for Industrial Applications of AC/AC Power Converters without DC-Link

“…The configuration of converter switches is selected to ensure a minimum value of the cost function [40]. Scientific publications with MPC of an MC show better achievements in the quality of current and voltage waveforms, torque ripple, and internal switching power losses [30,41,42]. An example MPC scheme for the MC is shown in Figure 16, and this example is one that minimizes the cost function related to the accuracy of shaping the output current and the input power factor.…”

Reduction of Prediction Errors for the Matrix Converter with an Improved Model Predictive Control

“…By substituting Equations (14) and (15) into (10) and (12), the integral system model for the conventional MPC scheme can be expressed as follows:…”

“…MPC can be applied to achieve Featuring fast dynamic response and multi-objective optimization, MPC has attracted attention from researchers in various fields of power converters [9], including MCs. MPC can be applied to achieve normal operations of the MC, such as input reactive power minimization [10], sinusoidal input and output currents [11], direct power control [12], among others [13]. As MPC has the capability of multi-objective optimization, features such as motor torque control [14][15][16][17], speed control [18], common-mode voltage elimination [19], and efficiency improvement [20] can be easily included in MPC.…”

“…The source current is[k+1], input voltage ui[k+1], and output current io[k+1] at the beginning of the (k+1)th sampling period are calculated from Equations (10) and (12), respectively.…”

Comparison and Interpretation Methods for Predictive Control of Mechanics

“…Zhang et al utilized sequential model predictive control for power electronic AC converter, and their research sought to improve a complicated weighting factor design and also computational burden applied to three-phase direct matrix conversion. The performance akin nominal model's predictive control was revealed [63]. Baždarić et al introduced approaches to the predictive control of a DC-DC boost converter in a comparison of the controllers that consider all of the current objectives and minimize the complexity of the online processing, which results in an alternative way of building the process model based on the fuzzy identification that contributes to the final objective.…”