Learning Control for Networked Stochastic Systems With Random Fading Communication
The learning control strategy is studied for networked stochastic systems, where the output and input data are transmitted through multiple independent fading channels. The traditional P-type learning control scheme is revised according to the specific fading positions, where the constant learning g...
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| Veröffentlicht in: | IEEE transactions on systems, man, and cybernetics. Systems man, and cybernetics. Systems, 2022-06, Vol.52 (6), p.3659-3670 |
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| container_title | IEEE transactions on systems, man, and cybernetics. Systems |
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| creator | Shen, Dong Qu, Ganggui Song, Qijiang |
| description | The learning control strategy is studied for networked stochastic systems, where the output and input data are transmitted through multiple independent fading channels. The traditional P-type learning control scheme is revised according to the specific fading positions, where the constant learning gain is replaced by a variable one to suppress the effect of various uncertainties. Strong convergence of the proposed scheme is established under random fading phenomena and system noise. The input error is shown convergent to zero as the cycle number increases. Two numerical examples demonstrate the applications of the proposed scheme. |
| doi_str_mv | 10.1109/TSMC.2021.3070848 |
| format | Article |
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Two numerical examples demonstrate the applications of the proposed scheme.</description><identifier>ISSN: 2168-2216</identifier><identifier>EISSN: 2168-2232</identifier><identifier>DOI: 10.1109/TSMC.2021.3070848</identifier><identifier>CODEN: ITSMFE</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Additives ; Almost sure convergence ; Convergence ; Fading ; Fading channels ; Indexes ; Learning ; learning control ; mean-square convergence ; networked stochastic systems ; Stochastic processes ; Stochastic systems ; Target tracking ; Uncertainty</subject><ispartof>IEEE transactions on systems, man, and cybernetics. Systems, 2022-06, Vol.52 (6), p.3659-3670</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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Two numerical examples demonstrate the applications of the proposed scheme.</description><subject>Additives</subject><subject>Almost sure convergence</subject><subject>Convergence</subject><subject>Fading</subject><subject>Fading channels</subject><subject>Indexes</subject><subject>Learning</subject><subject>learning control</subject><subject>mean-square convergence</subject><subject>networked stochastic systems</subject><subject>Stochastic processes</subject><subject>Stochastic systems</subject><subject>Target tracking</subject><subject>Uncertainty</subject><issn>2168-2216</issn><issn>2168-2232</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kF1LwzAYhYMoOOZ-gHgT8LozX02TSylOhamwTbwMSZq6zrWZSYbs39vRsav3XJznvPAAcIvRFGMkH1bLt3JKEMFTigokmLgAI4K5yAih5PKcMb8Gkxg3CCFMBKeIj8Bi7nTomu4blr5LwW9h7QN8d-nPhx9XwWXydq1jaixcHmJybYRfTVrDhe4q38KZrga2bfddY3VqfHcDrmq9jW5yumPwOXtalS_Z_OP5tXycZ5ZImjJtiZDGMIltbakVlcGSUUEKY7XE0lamoEXBGM-NMYjbWufUWUORdJKaHhmD-2F3F_zv3sWkNn4fuv6lIpzngmHMUd_CQ8sGH2NwtdqFptXhoDBSR3vqaE8d7amTvZ65G5jGOXfuS4Zy2i_-A7xFa-g</recordid><startdate>202206</startdate><enddate>202206</enddate><creator>Shen, Dong</creator><creator>Qu, Ganggui</creator><creator>Song, Qijiang</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>H8D</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0003-1063-1351</orcidid><orcidid>https://orcid.org/0000-0002-9276-1290</orcidid><orcidid>https://orcid.org/0000-0002-4242-4288</orcidid></search><sort><creationdate>202206</creationdate><title>Learning Control for Networked Stochastic Systems With Random Fading Communication</title><author>Shen, Dong ; Qu, Ganggui ; Song, Qijiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-ac289bb491cfc3c8db1943827bca919cdb73774465bbb06cfa53ecb309e93bfc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Additives</topic><topic>Almost sure convergence</topic><topic>Convergence</topic><topic>Fading</topic><topic>Fading channels</topic><topic>Indexes</topic><topic>Learning</topic><topic>learning control</topic><topic>mean-square convergence</topic><topic>networked stochastic systems</topic><topic>Stochastic processes</topic><topic>Stochastic systems</topic><topic>Target tracking</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shen, Dong</creatorcontrib><creatorcontrib>Qu, Ganggui</creatorcontrib><creatorcontrib>Song, Qijiang</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on systems, man, and cybernetics. 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| ispartof | IEEE transactions on systems, man, and cybernetics. Systems, 2022-06, Vol.52 (6), p.3659-3670 |
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| source | IEEE Electronic Library (IEL) |
| subjects | Additives Almost sure convergence Convergence Fading Fading channels Indexes Learning learning control mean-square convergence networked stochastic systems Stochastic processes Stochastic systems Target tracking Uncertainty |
| title | Learning Control for Networked Stochastic Systems With Random Fading Communication |
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