Multi‐innovation gradient parameter estimation for multivariable systems based on the maximum likelihood principle

Huafeng Xia; Sheng Xu; Cheng ZhouFeiyan Chen

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Multi‐innovation gradient parameter estimation for multivariable systems based on the maximum likelihood principle

机译：Multi‐innovation gradient parameter estimation for multivariable systems based on the maximum likelihood principle

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摘要

Abstract This article considers the parameter estimation problems of linear multivariable systems with unknown disturbances. For the parameter matrices in the multivariable systems, the model decomposition technique is used to reduce the computational complexity by decomposing the multivariable system into several subsystems with only the parameter vectors. By means of the negative gradient search, a decomposition‐based maximum likelihood recursive extended stochastic gradient algorithm is derived. In order to improve the parameter estimation accuracy, by introducing the multi‐innovation identification theory, a decomposition‐based maximum likelihood multi‐innovation extended stochastic gradient algorithm is proposed. The simulation results illustrate the effectiveness of the proposed algorithms.

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来源
《Optimal Control Applications and Methods》 |2022年第1期|106-122|共17页
作者
Huafeng Xia; Sheng Xu; Cheng ZhouFeiyan Chen;
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作者单位

Taizhou University;

Xi'an Jiaotong Liverpool University;

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原文格式 PDF
正文语种英语
中图分类应用数学;
关键词
decomposition technique; maximum likelihood; multi‐innovation identification theory; multivariable system; parameter estimation;

Multi‐innovation gradient parameter estimation for multivariable systems based on the maximum likelihood principle

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