Multi‐innovation gradient parameter estimation for multivariable systems based on the maximum likelihood principle
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 s...
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| Veröffentlicht in: | Optimal control applications & methods 2022-01, Vol.43 (1), p.106-122 |
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| creator | Xia, Huafeng Xu, Sheng Zhou, Cheng Chen, Feiyan |
| description | 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. |
| doi_str_mv | 10.1002/oca.2766 |
| format | Article |
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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. 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The simulation results illustrate the effectiveness of the proposed algorithms.</description><subject>Algorithms</subject><subject>Decomposition</subject><subject>decomposition technique</subject><subject>Innovations</subject><subject>maximum likelihood</subject><subject>multivariable system</subject><subject>multi‐innovation identification theory</subject><subject>Parameter estimation</subject><subject>Parameter identification</subject><subject>Subsystems</subject><issn>0143-2087</issn><issn>1099-1514</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp1kMtOAyEUhonRxFpNfAQSN26mAnMBlk3jLanpRtcEBsZSh2GEGbU7H8Fn9Emk1q2rszjffy4fAOcYzTBC5MrXckZoVR2ACUacZ7jExSGYIFzkGUGMHoOTGDcIIYpzMgHDw9gO9vvzy3adf5OD9R18DlJb0w2wl0E6M5gATRys23cbH6Dbhd5ksFK1BsZtHIyLUMloNEzIsDbQyQ_rRgdb-2Jau_Zewz7YrrZ9a07BUSPbaM7-6hQ83Vw_Lu6y5er2fjFfZjXheZUVsuS6KGVe1hohXhLd0BIV2jBWcsUapBjTHDNOC61UpXROKS0U5aSmKKXyKbjYz-2Dfx3TD2Ljx9CllYJUmOeYoYok6nJP1cHHGEwj0qFOhq3ASOyciuRU7JwmNNuj77Y12385sVrMf_kfdO967Q</recordid><startdate>202201</startdate><enddate>202201</enddate><creator>Xia, Huafeng</creator><creator>Xu, Sheng</creator><creator>Zhou, Cheng</creator><creator>Chen, Feiyan</creator><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0003-2360-1856</orcidid></search><sort><creationdate>202201</creationdate><title>Multi‐innovation gradient parameter estimation for multivariable systems based on the maximum likelihood principle</title><author>Xia, Huafeng ; Xu, Sheng ; Zhou, Cheng ; Chen, Feiyan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2936-4a59d45a35cd00952df7504de8859b8f0b88d918974dbb6bd37774b792c70a353</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Decomposition</topic><topic>decomposition technique</topic><topic>Innovations</topic><topic>maximum likelihood</topic><topic>multivariable system</topic><topic>multi‐innovation identification theory</topic><topic>Parameter estimation</topic><topic>Parameter identification</topic><topic>Subsystems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xia, Huafeng</creatorcontrib><creatorcontrib>Xu, Sheng</creatorcontrib><creatorcontrib>Zhou, Cheng</creatorcontrib><creatorcontrib>Chen, Feiyan</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Optimal control applications & methods</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xia, Huafeng</au><au>Xu, Sheng</au><au>Zhou, Cheng</au><au>Chen, Feiyan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multi‐innovation gradient parameter estimation for multivariable systems based on the maximum likelihood principle</atitle><jtitle>Optimal control applications & methods</jtitle><date>2022-01</date><risdate>2022</risdate><volume>43</volume><issue>1</issue><spage>106</spage><epage>122</epage><pages>106-122</pages><issn>0143-2087</issn><eissn>1099-1514</eissn><abstract>This article considers the parameter estimation problems of linear multivariable systems with unknown disturbances. 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| source | Wiley Online Library Journals Frontfile Complete |
| subjects | Algorithms Decomposition decomposition technique Innovations maximum likelihood multivariable system multi‐innovation identification theory Parameter estimation Parameter identification Subsystems |
| title | Multi‐innovation gradient parameter estimation for multivariable systems based on the maximum likelihood principle |
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