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<bibitem type="C">   <ARLID>0314253</ARLID> <utime>20240111140709.1</utime><mtime>20090326235959.9</mtime>   <WOS>000259261502130</WOS>         <title language="eng" primary="1">Merging of Multistep Predictors for Decentralized Adaptive Control</title>  <specification> <page_count>2 s.</page_count> <media_type>www</media_type> </specification>   <serial><ARLID>cav_un_epca*0314252</ARLID><ISBN>978-1-4244-2078-0</ISBN><title>Proceedings of the American Control Conference</title><part_num/><part_title/><page_num>3414-3415</page_num><publisher><place>Seattle</place><name>IEEE</name><year>2008</year></publisher></serial>   <title language="cze" primary="0">Míchání více-krokových prediktorů pro decentralizované adaptivní řízení</title>    <keyword>adaptive control</keyword>   <keyword>decentralised control</keyword>   <keyword>probability</keyword>    <author primary="1"> <ARLID>cav_un_auth*0101207</ARLID> <name1>Šmídl</name1> <name2>Václav</name2> <institution>UTIA-B</institution> <full_dept>Department of Adaptive Systems</full_dept>  <fullinstit>Ústav teorie informace a automatizace AV ČR, v. v. i.</fullinstit> </author> <author primary="0"> <ARLID>cav_un_auth*0101061</ARLID> <name1>Andrýsek</name1> <name2>Josef</name2> <institution>UTIA-B</institution>  <fullinstit>Ústav teorie informace a automatizace AV ČR, v. v. i.</fullinstit> </author>   <source> <source_type>pdf</source_type> <url>http://library.utia.cas.cz/separaty/2008/AS/smidl-merging of multistep predictors for decentralized adaptive.pdf</url> </source>        <cas_special> <project> <project_id>1M0572</project_id> <agency>GA MŠk</agency> <ARLID>cav_un_auth*0001814</ARLID> </project> <project> <project_id>GP102/08/P250</project_id> <agency>GA ČR</agency> <ARLID>cav_un_auth*0241640</ARLID> </project> <research> <research_id>CEZ:AV0Z10750506</research_id> </research>  <abstract language="eng" primary="1">Decentralized adaptive control is based on the use of many local controllers in parallel, each of them estimating its own local model and pursuing local aims. When each controller designs its strategy using only its model, the resulting control will be suboptimal since local models do not allow prediction of consequences of actions of the neighbors. We use probabilistic formulation of adaptive control to build predictive densities of future outputs. Mutual exchange of these densities on commonly observed variables is proposed to compensate for incompleteness of the local models. The task is to find a procedure how to use such information withing the control strategy design under the constraint that the resulting design procedure is of the same complexity as the one without the exchange. We present an approximate algorithm and illustrate its performance on a simple example.</abstract> <abstract language="cze" primary="0">Decentralizované adaptivní řízení předpolkádá, že každý uzel pozoruje své okolí a na základě svých pozorování vytváří svůj lokální model. Pokud navrhuje svoji strategii řízení na základě tohoto modelu, může dojít ke konfliktu se sousedním uzlem. Tento příspěvek nabízí řešení pomocí vícekrokových prediktorů, jež každý uzel generuje a posílá sousedním uzlům. Tyto prediktory se pak sjednotí pomocí technik  slučování pravdědpodobnostních distribucí. Funkčnost metody je demonstrována na jednoduchém příkladě.</abstract>  <action target="WRD"> <ARLID>cav_un_auth*0243572</ARLID> <name>American Control Conference</name> <place>Seattle</place> <dates>11.06.2008-13.06.2008</dates>  <country>US</country> </action>    <reportyear>2009</reportyear>  <RIV>BC</RIV>      <permalink>http://hdl.handle.net/11104/0164825</permalink>        <arlyear>2008</arlyear>       <unknown tag="mrcbU34"> 000259261502130 WOS </unknown> <unknown tag="mrcbU56"> pdf </unknown> <unknown tag="mrcbU63"> cav_un_epca*0314252 Proceedings of the American Control Conference 978-1-4244-2078-0 3414 3415 Seattle IEEE 2008 </unknown> </cas_special> </bibitem>