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Author:

Zhang, Dong (Zhang, Dong.) | Zhang, Yiming (Zhang, Yiming.) | Liang, Biao (Liang, Biao.) | Wang, He (Wang, He.)

Indexed by:

EI Scopus

Abstract:

Compared with the traditional PI control, the current loop based on model predictive control has better dynamic performance and is widely used in the field of power electronic control. Peak current control (PCC) can conveniently protect the peak current of inductance, avoid the saturation of inductance, and easily realize the over-current protection of converter. In this paper, a model predictive peak current control algorithm for real time inductance identification is proposed for BOOST converter, and a method of double sampling peak current and valley current is proposed to realize real time identification of inductance value in a sampling period, which improves the dynamic performance and robustness of the algorithm. Simulation and experiment show that the proposed method can identify the inductance value in real time and track the reference value of the inductance current in two control periods. © Published under licence by IOP Publishing Ltd.

Keyword:

Dynamics Model predictive control Inductance Electric current control DC-DC converters

Author Community:

  • [ 1 ] [Zhang, Dong]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Zhang, Yiming]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Liang, Biao]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Wang, He]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China

Reprint Author's Address:

  • [zhang, dong]faculty of information technology, beijing university of technology, beijing; 100124, china

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Source :

ISSN: 1742-6588

Year: 2021

Issue: 1

Volume: 1820

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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