Disturbance Observer Based Back-Stepping Control for Buck Converters with Unmatched Disturbances: An RBF Neural Network Approach

Wenbo Hao; Xinpo Lin; Weiliang Chen; You Ji; Fei Jing; Yabin Gao; Zhenhuan Wang; Jianxing Liu

doi:10.1109/CAC57257.2022.10055721

Disturbance Observer Based Back-Stepping Control for Buck Converters with Unmatched Disturbances: An RBF Neural Network Approach

Wenbo Hao
, Xinpo Lin
, Weiliang Chen
, You Ji
, Fei Jing
, Yabin Gao
, Zhenhuan Wang
, Jianxing Liu^*

^*Corresponding author for this work

Research Institute
Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

In this paper, a novel disturbance observer-based back-stepping control (DOBC) strategy combining with a radial basis function neural network (RBFNN) is proposed for DC-DC buck converters. Firstly, the state space average model of buck converter is established and it is turned into a general second-order model through coordinate transformation. Model parameter uncertainties and external disturbances are considered in two parts instead of a lumped disturbance. Based on the general second-order model of buck converter, RBFNN is used to estimate parameter uncertainties and a special disturbance observer is used to observe external disturbances. Then, back-stepping method is used to obtain the controller. Simulations are given to verify the advantages of the presented approach.

Original language	English
Title of host publication	Proceedings - 2022 Chinese Automation Congress, CAC 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3335-3340
Number of pages	6
ISBN (Electronic)	9781665465335
DOIs	https://doi.org/10.1109/CAC57257.2022.10055721
State	Published - 2022
Event	2022 Chinese Automation Congress, CAC 2022 - Xiamen, China Duration: 25 Nov 2022 → 27 Nov 2022

Publication series

Name	Proceedings - 2022 Chinese Automation Congress, CAC 2022
Volume	2022-January

Conference

Conference	2022 Chinese Automation Congress, CAC 2022
Country/Territory	China
City	Xiamen
Period	25/11/22 → 27/11/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Buck converter
disturbance observer
neural network
unmatched disturbances

Access to Document

10.1109/CAC57257.2022.10055721

Cite this

Hao, W., Lin, X., Chen, W., Ji, Y., Jing, F., Gao, Y., Wang, Z., & Liu, J. (2022). Disturbance Observer Based Back-Stepping Control for Buck Converters with Unmatched Disturbances: An RBF Neural Network Approach. In Proceedings - 2022 Chinese Automation Congress, CAC 2022 (pp. 3335-3340). (Proceedings - 2022 Chinese Automation Congress, CAC 2022; Vol. 2022-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAC57257.2022.10055721

Hao, Wenbo ; Lin, Xinpo ; Chen, Weiliang et al. / Disturbance Observer Based Back-Stepping Control for Buck Converters with Unmatched Disturbances : An RBF Neural Network Approach. Proceedings - 2022 Chinese Automation Congress, CAC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 3335-3340 (Proceedings - 2022 Chinese Automation Congress, CAC 2022).

@inproceedings{155fd12a3d124c5ba617ff0a9df821e3,

title = "Disturbance Observer Based Back-Stepping Control for Buck Converters with Unmatched Disturbances: An RBF Neural Network Approach",

abstract = "In this paper, a novel disturbance observer-based back-stepping control (DOBC) strategy combining with a radial basis function neural network (RBFNN) is proposed for DC-DC buck converters. Firstly, the state space average model of buck converter is established and it is turned into a general second-order model through coordinate transformation. Model parameter uncertainties and external disturbances are considered in two parts instead of a lumped disturbance. Based on the general second-order model of buck converter, RBFNN is used to estimate parameter uncertainties and a special disturbance observer is used to observe external disturbances. Then, back-stepping method is used to obtain the controller. Simulations are given to verify the advantages of the presented approach.",

keywords = "Buck converter, disturbance observer, neural network, unmatched disturbances",

author = "Wenbo Hao and Xinpo Lin and Weiliang Chen and You Ji and Fei Jing and Yabin Gao and Zhenhuan Wang and Jianxing Liu",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 Chinese Automation Congress, CAC 2022 ; Conference date: 25-11-2022 Through 27-11-2022",

year = "2022",

doi = "10.1109/CAC57257.2022.10055721",

language = "英语",

series = "Proceedings - 2022 Chinese Automation Congress, CAC 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3335--3340",

booktitle = "Proceedings - 2022 Chinese Automation Congress, CAC 2022",

address = "美国",

}

Hao, W, Lin, X, Chen, W, Ji, Y, Jing, F, Gao, Y , Wang, Z & Liu, J 2022, Disturbance Observer Based Back-Stepping Control for Buck Converters with Unmatched Disturbances: An RBF Neural Network Approach. in Proceedings - 2022 Chinese Automation Congress, CAC 2022. Proceedings - 2022 Chinese Automation Congress, CAC 2022, vol. 2022-January, Institute of Electrical and Electronics Engineers Inc., pp. 3335-3340, 2022 Chinese Automation Congress, CAC 2022, Xiamen, China, 25/11/22. https://doi.org/10.1109/CAC57257.2022.10055721

Disturbance Observer Based Back-Stepping Control for Buck Converters with Unmatched Disturbances: An RBF Neural Network Approach. / Hao, Wenbo; Lin, Xinpo; Chen, Weiliang et al.
Proceedings - 2022 Chinese Automation Congress, CAC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 3335-3340 (Proceedings - 2022 Chinese Automation Congress, CAC 2022; Vol. 2022-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Disturbance Observer Based Back-Stepping Control for Buck Converters with Unmatched Disturbances

T2 - 2022 Chinese Automation Congress, CAC 2022

AU - Hao, Wenbo

AU - Lin, Xinpo

AU - Chen, Weiliang

AU - Ji, You

AU - Jing, Fei

AU - Gao, Yabin

AU - Wang, Zhenhuan

AU - Liu, Jianxing

PY - 2022

Y1 - 2022

N2 - In this paper, a novel disturbance observer-based back-stepping control (DOBC) strategy combining with a radial basis function neural network (RBFNN) is proposed for DC-DC buck converters. Firstly, the state space average model of buck converter is established and it is turned into a general second-order model through coordinate transformation. Model parameter uncertainties and external disturbances are considered in two parts instead of a lumped disturbance. Based on the general second-order model of buck converter, RBFNN is used to estimate parameter uncertainties and a special disturbance observer is used to observe external disturbances. Then, back-stepping method is used to obtain the controller. Simulations are given to verify the advantages of the presented approach.

AB - In this paper, a novel disturbance observer-based back-stepping control (DOBC) strategy combining with a radial basis function neural network (RBFNN) is proposed for DC-DC buck converters. Firstly, the state space average model of buck converter is established and it is turned into a general second-order model through coordinate transformation. Model parameter uncertainties and external disturbances are considered in two parts instead of a lumped disturbance. Based on the general second-order model of buck converter, RBFNN is used to estimate parameter uncertainties and a special disturbance observer is used to observe external disturbances. Then, back-stepping method is used to obtain the controller. Simulations are given to verify the advantages of the presented approach.

KW - Buck converter

KW - disturbance observer

KW - neural network

KW - unmatched disturbances

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U2 - 10.1109/CAC57257.2022.10055721

DO - 10.1109/CAC57257.2022.10055721

M3 - 会议稿件

AN - SCOPUS:85151143655

T3 - Proceedings - 2022 Chinese Automation Congress, CAC 2022

SP - 3335

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BT - Proceedings - 2022 Chinese Automation Congress, CAC 2022

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 25 November 2022 through 27 November 2022

ER -

Hao W, Lin X, Chen W, Ji Y, Jing F, Gao Y et al. Disturbance Observer Based Back-Stepping Control for Buck Converters with Unmatched Disturbances: An RBF Neural Network Approach. In Proceedings - 2022 Chinese Automation Congress, CAC 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 3335-3340. (Proceedings - 2022 Chinese Automation Congress, CAC 2022). doi: 10.1109/CAC57257.2022.10055721

Disturbance Observer Based Back-Stepping Control for Buck Converters with Unmatched Disturbances: An RBF Neural Network Approach

Abstract

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UN SDGs

Keywords

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