典型文献
Recursive Least Squares Identification With Variable-Direction Forgetting via Oblique Projection Decomposition
文献摘要：
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.
文献关键词：
中图分类号：
作者姓名：
Kun Zhu;Chengpu Yu;Yiming Wan
作者机构：
Key Laboratory of Image Processing and Intelligent Control,School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,China;Beijing Institute of Technology Chongqing Innovation Center,Chongqing 401147,China
文献出处：
引用格式：
[1]Kun Zhu;Chengpu Yu;Yiming Wan-.Recursive Least Squares Identification With Variable-Direction Forgetting via Oblique Projection Decomposition)[J].自动化学报（英文版）,2022(03):547-555
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AB值：
0.644541
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