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摘要:
In this paper, a new recursive least squares (RLS) identification algorithm with variable-direction forgetting (VDF) is proposed for multi-output systems. The objective is to enhance parameter estimation performance under non-persistent excitation. The proposed algorithm performs oblique projection decomposition of the information matrix, such that forgetting is applied only to directions where new information is received. Theoretical proofs show that even without persistent excitation, the information matrix remains lower and upper bounded, and the estimation error variance converges to be within a finite bound. Moreover, detailed analysis is made to compare with a recently reported VDF algorithm that exploits eigenvalue decomposition (VDF-ED). It is revealed that under non-persistent excitation, part of the forgotten subspace in the VDF-ED algorithm could discount old information without receiving new data, which could produce a more ill-conditioned information matrix than our proposed algorithm. Numerical simulation results demonstrate the efficacy and advantage of our proposed algorithm over this recent VDF-ED algorithm.
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篇名 Recursive Least Squares Identification With Variable-Direction Forgetting via Oblique Projection Decomposition
来源期刊 自动化学报(英文版) 学科
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年,卷(期) 2022,(3) 所属期刊栏目 PAPERS
研究方向 页码范围 547-555
页数 9页 分类号
字数 语种 英文
DOI 10.1109/JAS.2021.1004362
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期刊影响力
自动化学报(英文版)
双月刊
2329-9266
10-1193/TP
大16开
北京市海淀区中关村东路95号
80-604
2014
eng
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801
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