Bootstrap control
In this paper, we present a new way to control linear stochastic systems. The method is based on statistical bootstrap techniques. The optimal future control signal is derived in such a way that unknown noise distribution and uncertainties in parameter estimates are taken into account. This is achie...
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| Veröffentlicht in: | IEEE transactions on automatic control 2006-01, Vol.51 (1), p.28-37 |
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| container_title | IEEE transactions on automatic control |
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| creator | Aronsson, M. Arvastson, L. Holst, J. Lindoff, B. Svensson, A. |
| description | In this paper, we present a new way to control linear stochastic systems. The method is based on statistical bootstrap techniques. The optimal future control signal is derived in such a way that unknown noise distribution and uncertainties in parameter estimates are taken into account. This is achieved by resampling from existing data when calculating statistical distributions of future process values. The bootstrap algorithm takes care of arbitrary loss functions and unknown noise distribution even for small estimation sets. The efficient way of utilizing data implies that the method is also well suited for slowly time-varying stochastic systems. |
| doi_str_mv | 10.1109/TAC.2005.861722 |
| format | Article |
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The method is based on statistical bootstrap techniques. The optimal future control signal is derived in such a way that unknown noise distribution and uncertainties in parameter estimates are taken into account. This is achieved by resampling from existing data when calculating statistical distributions of future process values. The bootstrap algorithm takes care of arbitrary loss functions and unknown noise distribution even for small estimation sets. The efficient way of utilizing data implies that the method is also well suited for slowly time-varying stochastic systems.</description><identifier>ISSN: 0018-9286</identifier><identifier>EISSN: 1558-2523</identifier><identifier>DOI: 10.1109/TAC.2005.861722</identifier><identifier>CODEN: IETAA9</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Algorithms ; Applied sciences ; Computer science; control theory; systems ; control ; Control systems ; Control theory. 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The method is based on statistical bootstrap techniques. The optimal future control signal is derived in such a way that unknown noise distribution and uncertainties in parameter estimates are taken into account. This is achieved by resampling from existing data when calculating statistical distributions of future process values. The bootstrap algorithm takes care of arbitrary loss functions and unknown noise distribution even for small estimation sets. The efficient way of utilizing data implies that the method is also well suited for slowly time-varying stochastic systems.</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Computer science; control theory; systems</subject><subject>control</subject><subject>Control systems</subject><subject>Control theory. Systems</subject><subject>Estimates</subject><subject>Exact sciences and technology</subject><subject>Feedback loop</subject><subject>Generalized predictive control</subject><subject>Matematik</subject><subject>Mathematical analysis</subject><subject>Mathematics</subject><subject>Modelling and identification</subject><subject>Natural Sciences</subject><subject>Naturvetenskap</subject><subject>Noise</subject><subject>Open loop systems</subject><subject>Optimal control</subject><subject>Optimization</subject><subject>Parameter estimation</subject><subject>Probability Theory and Statistics</subject><subject>Process control</subject><subject>quality control</subject><subject>resampling</subject><subject>Sannolikhetsteori och statistik</subject><subject>statistical bootstrap techniques</subject><subject>Statistical distributions</subject><subject>statistical process</subject><subject>statistical process control</subject><subject>stochastic control</subject><subject>Stochastic processes</subject><subject>Stochastic systems</subject><subject>Time varying systems</subject><subject>Uncertainty</subject><issn>0018-9286</issn><issn>1558-2523</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqNkU2LFDEQhoMoOK4ePHnwIoJ66tlUkqquPq6DXzDgZT2HdDqBXnonbdKN-O83Qy8ueBAPRVHkqbdS9QrxCuQeQHaX11eHvZIS90zQKvVI7ACRG4VKPxY7KYGbTjE9Fc9KuaklGQM78fJjSktZspvf-HRacpqeiyfRTSW8uM8X4sfnT9eHr83x-5dvh6tj443GpTF9COCREYEG6DusJcjQEfAQSHNnYtt60j1qNyBDz6S94aDCEFFH0BfiuOmWX2Feezvn8dbl3za50U7rXKOvYUuwwNQNkaKtStGaYdCWY9tb4wdiHQnJnOU-bHJzTj_XUBZ7OxYfpsmdQlqL5fojgwqpku__SSoGKVWn_gOsV2wVV_DtX-BNWvOpXs8ytbIzkrBClxvkcyolh_hnY5D27KCtDtqzg3ZzsHa8u5d1xbspZnfyY3loa41WROfxrzduDCE8PGNLjFrfAXF0oBE</recordid><startdate>200601</startdate><enddate>200601</enddate><creator>Aronsson, M.</creator><creator>Arvastson, L.</creator><creator>Holst, J.</creator><creator>Lindoff, B.</creator><creator>Svensson, A.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Systems</topic><topic>Estimates</topic><topic>Exact sciences and technology</topic><topic>Feedback loop</topic><topic>Generalized predictive control</topic><topic>Matematik</topic><topic>Mathematical analysis</topic><topic>Mathematics</topic><topic>Modelling and identification</topic><topic>Natural Sciences</topic><topic>Naturvetenskap</topic><topic>Noise</topic><topic>Open loop systems</topic><topic>Optimal control</topic><topic>Optimization</topic><topic>Parameter estimation</topic><topic>Probability Theory and Statistics</topic><topic>Process control</topic><topic>quality control</topic><topic>resampling</topic><topic>Sannolikhetsteori och statistik</topic><topic>statistical bootstrap techniques</topic><topic>Statistical distributions</topic><topic>statistical process</topic><topic>statistical process control</topic><topic>stochastic control</topic><topic>Stochastic processes</topic><topic>Stochastic systems</topic><topic>Time varying systems</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Aronsson, M.</creatorcontrib><creatorcontrib>Arvastson, L.</creatorcontrib><creatorcontrib>Holst, J.</creatorcontrib><creatorcontrib>Lindoff, B.</creatorcontrib><creatorcontrib>Svensson, A.</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>Pascal-Francis</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>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><collection>Aerospace Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>SwePub</collection><collection>SwePub Articles</collection><collection>SWEPUB Lunds universitet</collection><jtitle>IEEE transactions on automatic control</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Aronsson, M.</au><au>Arvastson, L.</au><au>Holst, J.</au><au>Lindoff, B.</au><au>Svensson, A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Bootstrap control</atitle><jtitle>IEEE transactions on automatic control</jtitle><stitle>TAC</stitle><date>2006-01</date><risdate>2006</risdate><volume>51</volume><issue>1</issue><spage>28</spage><epage>37</epage><pages>28-37</pages><issn>0018-9286</issn><eissn>1558-2523</eissn><coden>IETAA9</coden><abstract>In this paper, we present a new way to control linear stochastic systems. The method is based on statistical bootstrap techniques. The optimal future control signal is derived in such a way that unknown noise distribution and uncertainties in parameter estimates are taken into account. This is achieved by resampling from existing data when calculating statistical distributions of future process values. The bootstrap algorithm takes care of arbitrary loss functions and unknown noise distribution even for small estimation sets. The efficient way of utilizing data implies that the method is also well suited for slowly time-varying stochastic systems.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TAC.2005.861722</doi><tpages>10</tpages></addata></record> |
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| subjects | Algorithms Applied sciences Computer science control theory systems control Control systems Control theory. Systems Estimates Exact sciences and technology Feedback loop Generalized predictive control Matematik Mathematical analysis Mathematics Modelling and identification Natural Sciences Naturvetenskap Noise Open loop systems Optimal control Optimization Parameter estimation Probability Theory and Statistics Process control quality control resampling Sannolikhetsteori och statistik statistical bootstrap techniques Statistical distributions statistical process statistical process control stochastic control Stochastic processes Stochastic systems Time varying systems Uncertainty |
| title | Bootstrap control |
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