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Jie Hou1,Zhen Yang1,Taifu Li2,et al.[en_title][J].Control Theory and Technology,2024,22(2):173~183.[Copy]
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Full-parameter constrained parsimonious subspace identification with steady-state information for DC–DC converters
JieHou1,ZhenYang1,TaifuLi2,HuimingWang1,JinchengJiang1,XiaoleiChen1
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(1 College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;2 School of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing 401331, China)
摘要:
A full-parameter constrained parsimonious subspace identification method that incorporates the steady-state a priori information of the system is proposed to model the DC–DC converters. A parsimonious model with fewer parameters is used to represent the system, and then an optimal weighted methods is used to estimate the system parameters matrices by taking into account both dynamical data and steady-state data. Compared with traditional data-driven methods for DC–DC converters, the subspace-based method can simultaneously estimate model structure and parameter with appropriate computational complexity. Moreover, compared with the traditional full-parameter constrained subspace approach, the proposed algorithm can accurately estimate the system parameters with a smaller variance. The experimental results on a DC–DC synchronous buck converter verify the effectiveness and superiority of the proposed method.
关键词:  System identification · Constrained identification · DC–DC converters · Subspace identification
DOI:https://doi.org/10.1007/s11768-023-00148-9
基金项目:This work was supported in part by the Chongqing Natural Science Foundation (Nos. CSTB2022NSCQ-MSX1225, cstc2021jcyjmsxmX0142), in part by the Science and Technology Research Program of Chongqing Municipal Education Commission (Nos. KJQN202000602, KJQN202200626), in part by the National Natural Science Foundation of China (No. 61903057), in part by the China Postdoctoral Science Foundation (No. 2022MD713688) and in part by the Chongqing Postdoctoral Science Foundation (No. 2021XM3079).
Full-parameter constrained parsimonious subspace identification with steady-state information for DC–DC converters
Jie Hou1,Zhen Yang1,Taifu Li2,Huiming Wang1,Jincheng Jiang1,Xiaolei Chen1
(1 College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;2 School of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing 401331, China)
Abstract:
A full-parameter constrained parsimonious subspace identification method that incorporates the steady-state a priori information of the system is proposed to model the DC–DC converters. A parsimonious model with fewer parameters is used to represent the system, and then an optimal weighted methods is used to estimate the system parameters matrices by taking into account both dynamical data and steady-state data. Compared with traditional data-driven methods for DC–DC converters, the subspace-based method can simultaneously estimate model structure and parameter with appropriate computational complexity. Moreover, compared with the traditional full-parameter constrained subspace approach, the proposed algorithm can accurately estimate the system parameters with a smaller variance. The experimental results on a DC–DC synchronous buck converter verify the effectiveness and superiority of the proposed method.
Key words:  System identification · Constrained identification · DC–DC converters · Subspace identification