Quantised MPC for LPV systems by using new Lyapunov–Krasovskii functional
This study deals with the problem of sampled-data model predictive control (MPC) for linear parameter varying (LPV) systems with input quantisation. The LPV systems under consideration depend on a set of parameters that are bounded and available online. To deal with a piecewise constant sampled-data...
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| Veröffentlicht in: | IET control theory & applications 2017-02, Vol.11 (3), p.439-445 |
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| container_title | IET control theory & applications |
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| creator | Lee, Sangmoon Kwon, Ohmin |
| description | This study deals with the problem of sampled-data model predictive control (MPC) for linear parameter varying (LPV) systems with input quantisation. The LPV systems under consideration depend on a set of parameters that are bounded and available online. To deal with a piecewise constant sampled-data and quantisation of the control input, the closed-loop system is modelled as a continuous-time impulsive dynamic model with sector non-linearity. The control problem is formulated as a minimisation of the upper bound of infinite horizon cost function subject to a sufficient condition for stability. The stability of the proposed MPC is guaranteed by constructing new Lyapunov–Krasovskii functional. Finally, a numerical example is provided to illustrate the effectiveness and benefits of the proposed theoretical results. |
| doi_str_mv | 10.1049/iet-cta.2016.0597 |
| format | Article |
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The LPV systems under consideration depend on a set of parameters that are bounded and available online. To deal with a piecewise constant sampled-data and quantisation of the control input, the closed-loop system is modelled as a continuous-time impulsive dynamic model with sector non-linearity. The control problem is formulated as a minimisation of the upper bound of infinite horizon cost function subject to a sufficient condition for stability. The stability of the proposed MPC is guaranteed by constructing new Lyapunov–Krasovskii functional. Finally, a numerical example is provided to illustrate the effectiveness and benefits of the proposed theoretical results.</description><identifier>ISSN: 1751-8644</identifier><identifier>ISSN: 1751-8652</identifier><identifier>EISSN: 1751-8652</identifier><identifier>DOI: 10.1049/iet-cta.2016.0597</identifier><language>eng</language><publisher>The Institution of Engineering and Technology</publisher><subject>closed loop systems ; closed‐loop system ; Constants ; Construction costs ; continuous time systems ; continuous‐time impulsive dynamic model ; control input quantisation ; control nonlinearities ; Control systems ; Dynamic models ; Dynamical systems ; infinite horizon ; infinite horizon cost function ; linear parameter varying systems ; LPV systems ; Lyapunov‐Krasovskii functional ; Mathematical models ; Nonlinear dynamics ; Parameters ; piecewise constant sampled‐data ; predictive control ; quantised MPC ; Research Article ; sampled data systems ; sampled‐data model predictive control ; sector nonlinearity ; upper bound minimisation</subject><ispartof>IET control theory & applications, 2017-02, Vol.11 (3), p.439-445</ispartof><rights>The Institution of Engineering and Technology</rights><rights>2021 The Authors. IET Control Theory & Applications published by John Wiley & Sons, Ltd. on behalf of The Institution of Engineering and Technology</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4015-2ac1b358c57d9d8faa635623a35ac639bc4dd0bcf87873dfcfcf36652610db4b3</citedby><cites>FETCH-LOGICAL-c4015-2ac1b358c57d9d8faa635623a35ac639bc4dd0bcf87873dfcfcf36652610db4b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1049%2Fiet-cta.2016.0597$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1049%2Fiet-cta.2016.0597$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,11562,27924,27925,45574,45575,46052,46476</link.rule.ids><linktorsrc>$$Uhttps://onlinelibrary.wiley.com/doi/abs/10.1049%2Fiet-cta.2016.0597$$EView_record_in_Wiley-Blackwell$$FView_record_in_$$GWiley-Blackwell</linktorsrc></links><search><creatorcontrib>Lee, Sangmoon</creatorcontrib><creatorcontrib>Kwon, Ohmin</creatorcontrib><title>Quantised MPC for LPV systems by using new Lyapunov–Krasovskii functional</title><title>IET control theory & applications</title><description>This study deals with the problem of sampled-data model predictive control (MPC) for linear parameter varying (LPV) systems with input quantisation. The LPV systems under consideration depend on a set of parameters that are bounded and available online. To deal with a piecewise constant sampled-data and quantisation of the control input, the closed-loop system is modelled as a continuous-time impulsive dynamic model with sector non-linearity. The control problem is formulated as a minimisation of the upper bound of infinite horizon cost function subject to a sufficient condition for stability. The stability of the proposed MPC is guaranteed by constructing new Lyapunov–Krasovskii functional. Finally, a numerical example is provided to illustrate the effectiveness and benefits of the proposed theoretical results.</description><subject>closed loop systems</subject><subject>closed‐loop system</subject><subject>Constants</subject><subject>Construction costs</subject><subject>continuous time systems</subject><subject>continuous‐time impulsive dynamic model</subject><subject>control input quantisation</subject><subject>control nonlinearities</subject><subject>Control systems</subject><subject>Dynamic models</subject><subject>Dynamical systems</subject><subject>infinite horizon</subject><subject>infinite horizon cost function</subject><subject>linear parameter varying systems</subject><subject>LPV systems</subject><subject>Lyapunov‐Krasovskii functional</subject><subject>Mathematical models</subject><subject>Nonlinear dynamics</subject><subject>Parameters</subject><subject>piecewise constant sampled‐data</subject><subject>predictive control</subject><subject>quantised MPC</subject><subject>Research Article</subject><subject>sampled data systems</subject><subject>sampled‐data model predictive control</subject><subject>sector nonlinearity</subject><subject>upper bound minimisation</subject><issn>1751-8644</issn><issn>1751-8652</issn><issn>1751-8652</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNqFkL9OwzAQhy0EEqXwAGweYUixYzt_2ErV0qpBFKmwWo7jIJc0CXHSKhvvwBvyJDgKQgwIdMPd8PvudB8A5xiNMKLhlVa1I2sxchH2RoiF_gEYYJ9hJ_CYe_g9U3oMTozZIMSYR9kALB8akdfaqATerSYwLSoYrZ6gaU2ttgbGLWyMzp9hrvYwakXZ5MXu4-19WQlT7MyL1jBtclnrIhfZKThKRWbU2VcfgsfZdD2ZO9H97WIyjhxJEWaOKySOCQsk85MwCVIhPMI8lwjChPRIGEuaJCiWaeAHPklSaYt49g0PoySmMRmCi35vWRWvjTI132ojVZaJXBWN4TgIKEYucZGN4j4qq8KYSqW8rPRWVC3HiHfiuBXHrTjeieOdOMtc98xeZ6r9H-CT9dy9mSFEfGZhp4e72KZoKuvF_Hns8pf8Yrq2W8c_bpRJSj4BAGiSXQ</recordid><startdate>20170203</startdate><enddate>20170203</enddate><creator>Lee, Sangmoon</creator><creator>Kwon, Ohmin</creator><general>The Institution of Engineering and Technology</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20170203</creationdate><title>Quantised MPC for LPV systems by using new Lyapunov–Krasovskii functional</title><author>Lee, Sangmoon ; Kwon, Ohmin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4015-2ac1b358c57d9d8faa635623a35ac639bc4dd0bcf87873dfcfcf36652610db4b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>closed loop systems</topic><topic>closed‐loop system</topic><topic>Constants</topic><topic>Construction costs</topic><topic>continuous time systems</topic><topic>continuous‐time impulsive dynamic model</topic><topic>control input quantisation</topic><topic>control nonlinearities</topic><topic>Control systems</topic><topic>Dynamic models</topic><topic>Dynamical systems</topic><topic>infinite horizon</topic><topic>infinite horizon cost function</topic><topic>linear parameter varying systems</topic><topic>LPV systems</topic><topic>Lyapunov‐Krasovskii functional</topic><topic>Mathematical models</topic><topic>Nonlinear dynamics</topic><topic>Parameters</topic><topic>piecewise constant sampled‐data</topic><topic>predictive control</topic><topic>quantised MPC</topic><topic>Research Article</topic><topic>sampled data systems</topic><topic>sampled‐data model predictive control</topic><topic>sector nonlinearity</topic><topic>upper bound minimisation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lee, Sangmoon</creatorcontrib><creatorcontrib>Kwon, Ohmin</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</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><jtitle>IET control theory & applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lee, Sangmoon</au><au>Kwon, Ohmin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Quantised MPC for LPV systems by using new Lyapunov–Krasovskii functional</atitle><jtitle>IET control theory & applications</jtitle><date>2017-02-03</date><risdate>2017</risdate><volume>11</volume><issue>3</issue><spage>439</spage><epage>445</epage><pages>439-445</pages><issn>1751-8644</issn><issn>1751-8652</issn><eissn>1751-8652</eissn><abstract>This study deals with the problem of sampled-data model predictive control (MPC) for linear parameter varying (LPV) systems with input quantisation. The LPV systems under consideration depend on a set of parameters that are bounded and available online. To deal with a piecewise constant sampled-data and quantisation of the control input, the closed-loop system is modelled as a continuous-time impulsive dynamic model with sector non-linearity. The control problem is formulated as a minimisation of the upper bound of infinite horizon cost function subject to a sufficient condition for stability. The stability of the proposed MPC is guaranteed by constructing new Lyapunov–Krasovskii functional. Finally, a numerical example is provided to illustrate the effectiveness and benefits of the proposed theoretical results.</abstract><pub>The Institution of Engineering and Technology</pub><doi>10.1049/iet-cta.2016.0597</doi><tpages>7</tpages></addata></record> |
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| subjects | closed loop systems closed‐loop system Constants Construction costs continuous time systems continuous‐time impulsive dynamic model control input quantisation control nonlinearities Control systems Dynamic models Dynamical systems infinite horizon infinite horizon cost function linear parameter varying systems LPV systems Lyapunov‐Krasovskii functional Mathematical models Nonlinear dynamics Parameters piecewise constant sampled‐data predictive control quantised MPC Research Article sampled data systems sampled‐data model predictive control sector nonlinearity upper bound minimisation |
| title | Quantised MPC for LPV systems by using new Lyapunov–Krasovskii functional |
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