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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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| This Paper:Browse 566 Download 0 |
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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) |
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| 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 |
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| 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). |
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| 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 |
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