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Dynamic inverse optimization
While system identification has traditionally concentrated on identifying systems driven by explicit ordinary differential equations, the recent explosion in computational power has made feasible systems whose dynamics are partly driven by real-time optimization processes. Identification algorithms...
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container_end_page | 4727 vol.6 |
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creator | Gentry, S. Saligrama, V. Feron, E. |
description | While system identification has traditionally concentrated on identifying systems driven by explicit ordinary differential equations, the recent explosion in computational power has made feasible systems whose dynamics are partly driven by real-time optimization processes. Identification algorithms which could pinpoint the optimization parameters used to drive these closed-loop control systems would clearly find application to receding horizon controllers and other control processes which incorporate online optimization. This work describes a procedure which identifies the optimization parameters at work in many types of receding horizon controllers. If all the control and state constraints are known, then the problem may be recast as identification of objective parameters of a real-time, static optimization problem. Using the necessary conditions of optimality in some cases of interest, this problem is shown to be equivalent to solving a feasibility semi-definite program. In alternate setups, the necessary conditions of optimality lead to a formulation of the identification problem as a feasibility linear or integer program. |
doi_str_mv | 10.1109/ACC.2001.945728 |
format | conference_proceeding |
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Identification algorithms which could pinpoint the optimization parameters used to drive these closed-loop control systems would clearly find application to receding horizon controllers and other control processes which incorporate online optimization. This work describes a procedure which identifies the optimization parameters at work in many types of receding horizon controllers. If all the control and state constraints are known, then the problem may be recast as identification of objective parameters of a real-time, static optimization problem. Using the necessary conditions of optimality in some cases of interest, this problem is shown to be equivalent to solving a feasibility semi-definite program. 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Using the necessary conditions of optimality in some cases of interest, this problem is shown to be equivalent to solving a feasibility semi-definite program. In alternate setups, the necessary conditions of optimality lead to a formulation of the identification problem as a feasibility linear or integer program.</description><subject>Constraint optimization</subject><subject>Control systems</subject><subject>Differential equations</subject><subject>Explosions</subject><subject>Large-scale systems</subject><subject>Optimal control</subject><subject>Pressing</subject><subject>Process control</subject><subject>Real time systems</subject><subject>System identification</subject><issn>0743-1619</issn><issn>2378-5861</issn><isbn>9780780364950</isbn><isbn>0780364953</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2001</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj8tqwzAQRUUfUDfNulC6yA_IndFjpFkG9wmBbtp1kGIJVGon2KaQfn0NKVy4q3MPV4hbhBoR-GHdNLUCwJqNdcqfiUpp56X1hOdiyc7DHE2GLVyICpzREgn5SlyP49fMMRNU4v7x2Ieu7Fal_0nDmFb7w1S68humsu9vxGUO32Na_vdCfD4_fTSvcvP-8tasN7KgU5NMOWRybZgdLUN02VrgyCYSAVjrICtCMpkhk2l9jrtgwWLysU3MIeiFuDvtlpTS9jCULgzH7emX_gPcQz3b</recordid><startdate>2001</startdate><enddate>2001</enddate><creator>Gentry, S.</creator><creator>Saligrama, V.</creator><creator>Feron, E.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>2001</creationdate><title>Dynamic inverse optimization</title><author>Gentry, S. ; Saligrama, V. ; Feron, E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i172t-efaf67da074d90b7f5509b94b66005570f26164f90f64d8fbca5051e8bde99aa3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2001</creationdate><topic>Constraint optimization</topic><topic>Control systems</topic><topic>Differential equations</topic><topic>Explosions</topic><topic>Large-scale systems</topic><topic>Optimal control</topic><topic>Pressing</topic><topic>Process control</topic><topic>Real time systems</topic><topic>System identification</topic><toplevel>online_resources</toplevel><creatorcontrib>Gentry, S.</creatorcontrib><creatorcontrib>Saligrama, V.</creatorcontrib><creatorcontrib>Feron, E.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore (Online service)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Gentry, S.</au><au>Saligrama, V.</au><au>Feron, E.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Dynamic inverse optimization</atitle><btitle>Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148)</btitle><stitle>ACC</stitle><date>2001</date><risdate>2001</risdate><volume>6</volume><spage>4722</spage><epage>4727 vol.6</epage><pages>4722-4727 vol.6</pages><issn>0743-1619</issn><eissn>2378-5861</eissn><isbn>9780780364950</isbn><isbn>0780364953</isbn><abstract>While system identification has traditionally concentrated on identifying systems driven by explicit ordinary differential equations, the recent explosion in computational power has made feasible systems whose dynamics are partly driven by real-time optimization processes. Identification algorithms which could pinpoint the optimization parameters used to drive these closed-loop control systems would clearly find application to receding horizon controllers and other control processes which incorporate online optimization. This work describes a procedure which identifies the optimization parameters at work in many types of receding horizon controllers. If all the control and state constraints are known, then the problem may be recast as identification of objective parameters of a real-time, static optimization problem. Using the necessary conditions of optimality in some cases of interest, this problem is shown to be equivalent to solving a feasibility semi-definite program. In alternate setups, the necessary conditions of optimality lead to a formulation of the identification problem as a feasibility linear or integer program.</abstract><pub>IEEE</pub><doi>10.1109/ACC.2001.945728</doi></addata></record> |
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ispartof | Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), 2001, Vol.6, p.4722-4727 vol.6 |
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source | IEEE Xplore All Conference Series |
subjects | Constraint optimization Control systems Differential equations Explosions Large-scale systems Optimal control Pressing Process control Real time systems System identification |
title | Dynamic inverse optimization |
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