Cluster Computing

, Volume 22, Supplement 3, pp 6633–6646 | Cite as

The research of grid-connected inverter control based on decaying amplification aggregation strategy in predictive control

  • Wenbo Chen
  • Dewei LiEmail author
  • Yugeng Xi
  • Yuli Xu
  • Zhongxue Gan


The Grid-connected inverter control strategies are becoming a more attractive subject as the growing requirements for renewable power. The model predictive control (MPC) strategy is thought as a prospective method for the control of inverters due to its capability of handling constraints and nonlinearity properly. However, the complicated computations at each sampling instant makes MPC hardly applied. For the fast implementing of MPC on grid connected inverter control, in this paper, a decaying amplification aggregation strategy based model predictive control is presented on three-phase LCL-filter Grid Connected Inverter control. Additionally, a cumulative incremental state space model is used to depress the possible error. The proposed algorithms can significantly reduce the online computational complexity of MPC and have an infinite prediction horizon. The stability is also discussed and simulation results of a 20 KW grid-connected inverter demonstrate the effectiveness and fast calculation ability of this strategy.


Grid-connected inverter Model predictive control (MPC) Decaying amplification aggregation MPC (DAA-MPC) 



This work is supported by the National Science Foundation of China (Grant Nos. 61463037, 61521063, 61590924) and National Program on Key Basic Research Project of China (973 Program, Grant No. 2014CB249200).


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Wenbo Chen
    • 1
    • 2
  • Dewei Li
    • 1
    Email author
  • Yugeng Xi
    • 1
  • Yuli Xu
    • 3
  • Zhongxue Gan
    • 3
  1. 1.Key Laboratory of System Control and Information Processing, Ministry of Education, Department of AutomationShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Shanghai Institute of TechnologyShanghaiChina
  3. 3.ENN Science &Technology Development Co., LtdLangfangChina

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