Inexact-Uzawa Primal-Dual Solver for Embedded Model Predictive Control
In this letter, we propose an inexact-Uzawa solver for embedded linear model predictive control (MPC). The inexact-Uzawa algorithm falls into the general framework of first-order primal-dual methods but employs both proximal-point and matrix splitting schemes to derive a numerically robust algorithm...
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| Veröffentlicht in: | IEEE control systems letters 2023, Vol.7, p.697-702 |
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| creator | Adegbege, Ambrose A. Harish, Nia |
| description | In this letter, we propose an inexact-Uzawa solver for embedded linear model predictive control (MPC). The inexact-Uzawa algorithm falls into the general framework of first-order primal-dual methods but employs both proximal-point and matrix splitting schemes to derive a numerically robust algorithm with \mathcal {O} ( 1/k ) convergence rate in the primal-dual gap to some saddle-point solution where k is the iteration count. Numerical MPC example shows the efficiency and the ease of implementation of the algorithm as compared to other related methods in the literature. |
| doi_str_mv | 10.1109/LCSYS.2022.3220594 |
| format | Article |
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The inexact-Uzawa algorithm falls into the general framework of first-order primal-dual methods but employs both proximal-point and matrix splitting schemes to derive a numerically robust algorithm with <inline-formula> <tex-math notation="LaTeX">\mathcal {O} </tex-math></inline-formula>(<inline-formula> <tex-math notation="LaTeX">1/k </tex-math></inline-formula>) convergence rate in the primal-dual gap to some saddle-point solution where <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula> is the iteration count. 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The inexact-Uzawa algorithm falls into the general framework of first-order primal-dual methods but employs both proximal-point and matrix splitting schemes to derive a numerically robust algorithm with <inline-formula> <tex-math notation="LaTeX">\mathcal {O} </tex-math></inline-formula>(<inline-formula> <tex-math notation="LaTeX">1/k </tex-math></inline-formula>) convergence rate in the primal-dual gap to some saddle-point solution where <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula> is the iteration count. Numerical MPC example shows the efficiency and the ease of implementation of the algorithm as compared to other related methods in the literature.]]></description><subject>Approximation algorithms</subject><subject>Convergence</subject><subject>Convex functions</subject><subject>embedded model predictive control</subject><subject>Inexact Uzawa methods</subject><subject>Minimization</subject><subject>Prediction algorithms</subject><subject>Predictive control</subject><subject>primal-dual iterative methods</subject><subject>Quadratic programming</subject><issn>2475-1456</issn><issn>2475-1456</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkN1Kw0AQhRdRsNS-gN7kBVJndidN9lJiq4WKQuyFV2E3OwuRtJFNrD9Pb2OLeHXm4nzD4RPiEmGKCPp6lRcvxVSClFMlJSSaTsRIUprESMns9N99LiZd9woAmMkUpB6JxXLLn6bq4_W3-TDRU6g3polv300TFW2z4xD5NkTzjWXn2EUPreNm32JXV3294yhvt31omwtx5k3T8eSYY7FezJ_z-3j1eLfMb1ZxhZT1MUJqFaQKtUUCmFWE3iaUGVcZaW3iVIJGo9w3SFoAzhRa8p5mhKyVU2MhD3-r0HZdYF--DYvDV4lQDjLKXxnlIKM8ythDVweoZuY_QGtCRaR-AHrVWd4</recordid><startdate>2023</startdate><enddate>2023</enddate><creator>Adegbege, Ambrose A.</creator><creator>Harish, Nia</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0002-2506-644X</orcidid><orcidid>https://orcid.org/0000-0003-4026-2374</orcidid></search><sort><creationdate>2023</creationdate><title>Inexact-Uzawa Primal-Dual Solver for Embedded Model Predictive Control</title><author>Adegbege, Ambrose A. ; Harish, Nia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c148t-107b307319b14006c41fb548adca2bb5d351a91273142b00e831b4ff4641e93d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Approximation algorithms</topic><topic>Convergence</topic><topic>Convex functions</topic><topic>embedded model predictive control</topic><topic>Inexact Uzawa methods</topic><topic>Minimization</topic><topic>Prediction algorithms</topic><topic>Predictive control</topic><topic>primal-dual iterative methods</topic><topic>Quadratic programming</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Adegbege, Ambrose A.</creatorcontrib><creatorcontrib>Harish, Nia</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><jtitle>IEEE control systems letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Adegbege, Ambrose A.</au><au>Harish, Nia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Inexact-Uzawa Primal-Dual Solver for Embedded Model Predictive Control</atitle><jtitle>IEEE control systems letters</jtitle><stitle>LCSYS</stitle><date>2023</date><risdate>2023</risdate><volume>7</volume><spage>697</spage><epage>702</epage><pages>697-702</pages><issn>2475-1456</issn><eissn>2475-1456</eissn><coden>ICSLBO</coden><abstract><![CDATA[In this letter, we propose an inexact-Uzawa solver for embedded linear model predictive control (MPC). The inexact-Uzawa algorithm falls into the general framework of first-order primal-dual methods but employs both proximal-point and matrix splitting schemes to derive a numerically robust algorithm with <inline-formula> <tex-math notation="LaTeX">\mathcal {O} </tex-math></inline-formula>(<inline-formula> <tex-math notation="LaTeX">1/k </tex-math></inline-formula>) convergence rate in the primal-dual gap to some saddle-point solution where <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula> is the iteration count. Numerical MPC example shows the efficiency and the ease of implementation of the algorithm as compared to other related methods in the literature.]]></abstract><pub>IEEE</pub><doi>10.1109/LCSYS.2022.3220594</doi><tpages>6</tpages><orcidid>https://orcid.org/0000-0002-2506-644X</orcidid><orcidid>https://orcid.org/0000-0003-4026-2374</orcidid></addata></record> |
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| subjects | Approximation algorithms Convergence Convex functions embedded model predictive control Inexact Uzawa methods Minimization Prediction algorithms Predictive control primal-dual iterative methods Quadratic programming |
| title | Inexact-Uzawa Primal-Dual Solver for Embedded Model Predictive Control |
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