Data-driven optimal tracking control for discrete-time systems with delays using adaptive dynamic programming
In this paper, the data-driven adaptive dynamic programming (ADP) algorithm is proposed to deal with the optimal tracking problem for the general discrete-time (DT) systems with delays for the first time. The model-free ADP algorithm is presented by using only the system’s input, output and the refe...
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| Veröffentlicht in: | Journal of the Franklin Institute 2018-09, Vol.355 (13), p.5649-5666 |
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| creator | Liu, Yang Zhang, Huaguang Yu, Rui Qu, Qiuxia |
| description | In this paper, the data-driven adaptive dynamic programming (ADP) algorithm is proposed to deal with the optimal tracking problem for the general discrete-time (DT) systems with delays for the first time. The model-free ADP algorithm is presented by using only the system’s input, output and the reference trajectory of the finite steps of historical data. First, the augmented state equation is constructed based on the time-delay system and the reference system. Second, a novel data-driven state equation is derived by virtue of the history data composed of input, output and reference trajectory, which is considered as a state estimator.Then, a novel data-driven Bellman equation for the linear quadratic tracking (LQT) problem with delays is deduced. Finally, the data-driven ADP algorithm is designed to solve the LQT problem with delays and does not require any system dynamics. The simulation result demonstrates the validity of the proposed data-driven ADP algorithm in this paper for the LQT problem with delays. |
| doi_str_mv | 10.1016/j.jfranklin.2018.06.013 |
| format | Article |
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The model-free ADP algorithm is presented by using only the system’s input, output and the reference trajectory of the finite steps of historical data. First, the augmented state equation is constructed based on the time-delay system and the reference system. Second, a novel data-driven state equation is derived by virtue of the history data composed of input, output and reference trajectory, which is considered as a state estimator.Then, a novel data-driven Bellman equation for the linear quadratic tracking (LQT) problem with delays is deduced. Finally, the data-driven ADP algorithm is designed to solve the LQT problem with delays and does not require any system dynamics. The simulation result demonstrates the validity of the proposed data-driven ADP algorithm in this paper for the LQT problem with delays.</description><identifier>ISSN: 0016-0032</identifier><identifier>EISSN: 1879-2693</identifier><identifier>EISSN: 0016-0032</identifier><identifier>DOI: 10.1016/j.jfranklin.2018.06.013</identifier><language>eng</language><publisher>Elmsford: Elsevier Ltd</publisher><subject>Adaptive algorithms ; Adaptive control ; Computer simulation ; Discrete time systems ; Dynamic programming ; Equations of state ; Reference systems ; System dynamics ; Systems management ; Time delay systems ; Tracking control ; Tracking control systems ; Tracking problem ; Trajectories</subject><ispartof>Journal of the Franklin Institute, 2018-09, Vol.355 (13), p.5649-5666</ispartof><rights>2018</rights><rights>Copyright Elsevier Science Ltd. 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The model-free ADP algorithm is presented by using only the system’s input, output and the reference trajectory of the finite steps of historical data. First, the augmented state equation is constructed based on the time-delay system and the reference system. Second, a novel data-driven state equation is derived by virtue of the history data composed of input, output and reference trajectory, which is considered as a state estimator.Then, a novel data-driven Bellman equation for the linear quadratic tracking (LQT) problem with delays is deduced. Finally, the data-driven ADP algorithm is designed to solve the LQT problem with delays and does not require any system dynamics. The simulation result demonstrates the validity of the proposed data-driven ADP algorithm in this paper for the LQT problem with delays.</description><subject>Adaptive algorithms</subject><subject>Adaptive control</subject><subject>Computer simulation</subject><subject>Discrete time systems</subject><subject>Dynamic programming</subject><subject>Equations of state</subject><subject>Reference systems</subject><subject>System dynamics</subject><subject>Systems management</subject><subject>Time delay systems</subject><subject>Tracking control</subject><subject>Tracking control systems</subject><subject>Tracking problem</subject><subject>Trajectories</subject><issn>0016-0032</issn><issn>1879-2693</issn><issn>0016-0032</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNqFkEtPwzAQhC0EEqXwG7DEOcFrJ05zrMpTqsSld8u1N8VpHsV2i_rvcVXEldNqtTPfaoaQe2A5MJCPbd42Xg_bzg05ZzDLmcwZiAsygVlVZ1zW4pJMWJJmjAl-TW5CaNNaAWMT0j_pqDPr3QEHOu6i63VHo9dm64YNNeMQ_djRZvTUumA8RsySBmk4hoh9oN8uflKLnT4Gug8nj7Y6YQ5I7XHQvTN058eN132fjrfkqtFdwLvfOSWrl-fV4i1bfry-L-bLzIhCxMyUFUDVCBCsrOpacDBNNZNQybUtOMPSApSmBLOWpWaNqEuujdVlITSXyMWUPJyx6fXXHkNU7bj3Q_qoOEAigCxEUlVnlfFjCB4btfMpvj8qYOpUrWrVX7XqVK1iUqVqk3N-dmLKcHDoVTAOB4PWeTRR2dH9y_gBE0CH-A</recordid><startdate>201809</startdate><enddate>201809</enddate><creator>Liu, Yang</creator><creator>Zhang, Huaguang</creator><creator>Yu, Rui</creator><creator>Qu, Qiuxia</creator><general>Elsevier Ltd</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope><orcidid>https://orcid.org/0000-0002-8022-907X</orcidid></search><sort><creationdate>201809</creationdate><title>Data-driven optimal tracking control for discrete-time systems with delays using adaptive dynamic programming</title><author>Liu, Yang ; Zhang, Huaguang ; Yu, Rui ; Qu, Qiuxia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c343t-c57117f31305799321cf786176bd420e5d115c51cb65a0f3952acda543a26e23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adaptive algorithms</topic><topic>Adaptive control</topic><topic>Computer simulation</topic><topic>Discrete time systems</topic><topic>Dynamic programming</topic><topic>Equations of state</topic><topic>Reference systems</topic><topic>System dynamics</topic><topic>Systems management</topic><topic>Time delay systems</topic><topic>Tracking control</topic><topic>Tracking control systems</topic><topic>Tracking problem</topic><topic>Trajectories</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Yang</creatorcontrib><creatorcontrib>Zhang, Huaguang</creatorcontrib><creatorcontrib>Yu, Rui</creatorcontrib><creatorcontrib>Qu, Qiuxia</creatorcontrib><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>Journal of the Franklin Institute</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Yang</au><au>Zhang, Huaguang</au><au>Yu, Rui</au><au>Qu, Qiuxia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Data-driven optimal tracking control for discrete-time systems with delays using adaptive dynamic programming</atitle><jtitle>Journal of the Franklin Institute</jtitle><date>2018-09</date><risdate>2018</risdate><volume>355</volume><issue>13</issue><spage>5649</spage><epage>5666</epage><pages>5649-5666</pages><issn>0016-0032</issn><eissn>1879-2693</eissn><eissn>0016-0032</eissn><abstract>In this paper, the data-driven adaptive dynamic programming (ADP) algorithm is proposed to deal with the optimal tracking problem for the general discrete-time (DT) systems with delays for the first time. The model-free ADP algorithm is presented by using only the system’s input, output and the reference trajectory of the finite steps of historical data. First, the augmented state equation is constructed based on the time-delay system and the reference system. Second, a novel data-driven state equation is derived by virtue of the history data composed of input, output and reference trajectory, which is considered as a state estimator.Then, a novel data-driven Bellman equation for the linear quadratic tracking (LQT) problem with delays is deduced. Finally, the data-driven ADP algorithm is designed to solve the LQT problem with delays and does not require any system dynamics. The simulation result demonstrates the validity of the proposed data-driven ADP algorithm in this paper for the LQT problem with delays.</abstract><cop>Elmsford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.jfranklin.2018.06.013</doi><tpages>18</tpages><orcidid>https://orcid.org/0000-0002-8022-907X</orcidid></addata></record> |
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| subjects | Adaptive algorithms Adaptive control Computer simulation Discrete time systems Dynamic programming Equations of state Reference systems System dynamics Systems management Time delay systems Tracking control Tracking control systems Tracking problem Trajectories |
| title | Data-driven optimal tracking control for discrete-time systems with delays using adaptive dynamic programming |
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