On the Quadratic Programming Solution for Model Predictive Control with Move Blocking
Model Predictive Control (MPC) is a popular optimization-based control technique. MPC is usually formulated as sparse or dense Quadratic Programming (QP). This paper reviews two well-known methods, namely, state condensing and move blocking, and brings them together. Their combination results in gen...
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| creator | Otta, Pavel Santin, Ondrej Havlena, Vladimir |
| description | Model Predictive Control (MPC) is a popular optimization-based control
technique. MPC is usually formulated as sparse or dense Quadratic Programming
(QP). This paper reviews two well-known methods, namely, state condensing and
move blocking, and brings them together. Their combination results in
generalized QP that serves arbitrarily sparse (or dense) QP for MPC with move
blocking. The proposed QP can be solved by a specialized solver capable of
exploiting a sparsity structure of the problem. Numerical examples give inside
in computational and memory requirements. |
| doi_str_mv | 10.48550/arxiv.2002.06835 |
| format | Article |
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technique. MPC is usually formulated as sparse or dense Quadratic Programming
(QP). This paper reviews two well-known methods, namely, state condensing and
move blocking, and brings them together. Their combination results in
generalized QP that serves arbitrarily sparse (or dense) QP for MPC with move
blocking. The proposed QP can be solved by a specialized solver capable of
exploiting a sparsity structure of the problem. Numerical examples give inside
in computational and memory requirements.</description><identifier>DOI: 10.48550/arxiv.2002.06835</identifier><language>eng</language><subject>Computer Science - Systems and Control</subject><creationdate>2020-02</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2002.06835$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2002.06835$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Otta, Pavel</creatorcontrib><creatorcontrib>Santin, Ondrej</creatorcontrib><creatorcontrib>Havlena, Vladimir</creatorcontrib><title>On the Quadratic Programming Solution for Model Predictive Control with Move Blocking</title><description>Model Predictive Control (MPC) is a popular optimization-based control
technique. MPC is usually formulated as sparse or dense Quadratic Programming
(QP). This paper reviews two well-known methods, namely, state condensing and
move blocking, and brings them together. Their combination results in
generalized QP that serves arbitrarily sparse (or dense) QP for MPC with move
blocking. The proposed QP can be solved by a specialized solver capable of
exploiting a sparsity structure of the problem. Numerical examples give inside
in computational and memory requirements.</description><subject>Computer Science - Systems and Control</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj8tOwzAURL1hgQofwAr_QML1K3aWEPGSWhVEWUd-thZJjIxb4O8JhdVIczQjHYQuCNRcCQFXOn_FQ00BaA2NYuIUva4nXHYeP--1y7pEi59y2mY9jnHa4pc07EtMEw4p41Vyfpixd9GWePC4S1PJacCfsexmOjc3Q7Jv8_AMnQQ9fPjz_1ygzd3tpnuoluv7x-56WelGikoZzRhtpZXGCzDgiRLBK2IpaEEcsUwGyltoDTScEseDbZQkXBplSasYW6DLv9ujV_-e46jzd__r1x_92A_HD0qQ</recordid><startdate>20200217</startdate><enddate>20200217</enddate><creator>Otta, Pavel</creator><creator>Santin, Ondrej</creator><creator>Havlena, Vladimir</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20200217</creationdate><title>On the Quadratic Programming Solution for Model Predictive Control with Move Blocking</title><author>Otta, Pavel ; Santin, Ondrej ; Havlena, Vladimir</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a675-8ba33297c7be50b0e185fe81c20a51d1c37f24909b06421d4fc687147b8c19833</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Computer Science - Systems and Control</topic><toplevel>online_resources</toplevel><creatorcontrib>Otta, Pavel</creatorcontrib><creatorcontrib>Santin, Ondrej</creatorcontrib><creatorcontrib>Havlena, Vladimir</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Otta, Pavel</au><au>Santin, Ondrej</au><au>Havlena, Vladimir</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>On the Quadratic Programming Solution for Model Predictive Control with Move Blocking</atitle><date>2020-02-17</date><risdate>2020</risdate><abstract>Model Predictive Control (MPC) is a popular optimization-based control
technique. MPC is usually formulated as sparse or dense Quadratic Programming
(QP). This paper reviews two well-known methods, namely, state condensing and
move blocking, and brings them together. Their combination results in
generalized QP that serves arbitrarily sparse (or dense) QP for MPC with move
blocking. The proposed QP can be solved by a specialized solver capable of
exploiting a sparsity structure of the problem. Numerical examples give inside
in computational and memory requirements.</abstract><doi>10.48550/arxiv.2002.06835</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Systems and Control |
| title | On the Quadratic Programming Solution for Model Predictive Control with Move Blocking |
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