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author:

Yu, Xinhong (Yu, Xinhong.) [1] | Jiang, Tiantian (Jiang, Tiantian.) [2] | Xu, Libin (Xu, Libin.) [3] | Wei, Yao (Wei, Yao.) [4] | Wang, Fengxiang (Wang, Fengxiang.) [5] | Rodriguez, Jose (Rodriguez, Jose.) [6]

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EI

Abstract:

Three-phase interleaved boost converter has the characteristics of small input current ripple and high power density. However, unknown external disturbances, temperature change can cause inductive device parameter mismatch and control performance degradation. To solve this problem, this paper proposes a model predictive control method based on compensation function observer (CFO). Firstly, the mathematical model of the boost converter is established, and a predictive model including parameter disturbance terms is constructed. Secondly, the CFO is designed to observe the disturbance terms in the system and compensate for the fixed errors of the traditional observer, thereby improving the accuracy of the system. Thirdly, to achieve better dynamic performance, both the inner and outer loops employ model predictive control (MPC). Finally, the effectiveness and superiority of the proposed method were validated through experimental tests on the dynamic performance and robustness of the boost converter. © 2023 IEEE.

Keyword:

Boost converter Model predictive control Power electronics Predictive control systems

Community:

  • [ 1 ] [Yu, Xinhong]Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences, Quanzhou, China
  • [ 2 ] [Jiang, Tiantian]College of Advanced Manufacturing, Fuzhou University, Fuzhou, China
  • [ 3 ] [Xu, Libin]College of Electrical, Fuzhou University, Fuzhou, China
  • [ 4 ] [Wei, Yao]Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences, Quanzhou, China
  • [ 5 ] [Wang, Fengxiang]Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences, Quanzhou, China
  • [ 6 ] [Rodriguez, Jose]Universidad San Sebastian, Santiago, Chile

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Year: 2023

Page: 83-88

Language: English

Cited Count:

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ESI Highly Cited Papers on the List: 0 Unfold All

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30 Days PV: 3

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