A Novel Repetitive Model Predictive Control for Single-Phase Grid-Connected Inverters

Model predictive control (MPC) is increasingly used in single-phase grid-connected inverters (GCIs) due to its superior dynamic performance and ease of implementation in digital control systems. However, MPC urgently needs to solve the problem of poor steady-state performance and existent steady-state error. This paper proposed a novel repetitive MPC (NRMPC) to significantly improve steady-state performance and achieve zero steady-state error. And in this paper, the design of NRMPC is presented, and its effectiveness is verified by a simulation model of a 1.5 kW single-phase GCI, which demonstrates its potential in improving the performance of single-phase GCI systems.

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Publication Details

Published
2025-09-19
DOI
https://doi.org/10.1109/icamechs68051.2025.11181024
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
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A Novel Repetitive Model Predictive Control for Single-Phase Grid-Connected Inverters

Hao Pan, Qiangsong Zhao, Qifan Wang, Aihui Wang et al.
Microgrid Control and Optimization
article

A Novel Repetitive Model Predictive Control for Single-Phase Grid-Connected Inverters

Hao Pan, Qiangsong Zhao, Qifan Wang, Aihui Wang, Yan Wang
article en

Abstract

Model predictive control (MPC) is increasingly used in single-phase grid-connected inverters (GCIs) due to its superior dynamic performance and ease of implementation in digital control systems. However, MPC urgently needs to solve the problem of poor steady-state performance and existent steady-state error. This paper proposed a novel repetitive MPC (NRMPC) to significantly improve steady-state performance and achieve zero steady-state error. And in this paper, the design of NRMPC is presented, and its effectiveness is verified by a simulation model of a 1.5 kW single-phase GCI, which demonstrates its potential in improving the performance of single-phase GCI systems.

Zhongyuan University of Technology (CN)
Openalex Percentile: Top 65%
Microgrid Control and Optimization
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