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Filtered predictive control design using multi-objective optimization based on genetic algorithm for handling offset in chemical processes
[Display omitted] •GPC using multi-objective optimization for handling offset in chemical processes.•Filtered predictive control to deal with offset in chemical processes (FP-GPC).•Multi-objective optimization based on genetic algorithm to find optimal tuning.•Handling reference tracking and disturb...
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Published in: | Chemical engineering research & design 2017-01, Vol.117, p.265-273 |
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container_title | Chemical engineering research & design |
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creator | Araújo, Rejane de B. Coelho, Antonio A.R. |
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•GPC using multi-objective optimization for handling offset in chemical processes.•Filtered predictive control to deal with offset in chemical processes (FP-GPC).•Multi-objective optimization based on genetic algorithm to find optimal tuning.•Handling reference tracking and disturbance rejection.•Case study in chemical processes.
The purpose of this paper is to present the linear filtered positional generalized predictive controller (GPC) synthesis using both a positional process model and cost function to ensure stability and offset-free behavior (reference tracking and disturbance rejection), which involves selecting an integral polynomial weighting filter for the setpoint and output of the process, thereby extending the applicability of the predictive controllers to different reference shapes and step disturbances for handling chemical processes. Additionally, robustness aspects are incorporated into the control design of the weighting polynomials, an implementation which involves the filter tuning parameters using a multi-objective optimization based on genetic algorithm. Numerical simulations are conducted featuring two nonlinear chemical processes models (CSTR and boiler level) to assess the efficiency, stability and robustness of different reference shapes and load disturbance rejection. |
doi_str_mv | 10.1016/j.cherd.2016.10.038 |
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•GPC using multi-objective optimization for handling offset in chemical processes.•Filtered predictive control to deal with offset in chemical processes (FP-GPC).•Multi-objective optimization based on genetic algorithm to find optimal tuning.•Handling reference tracking and disturbance rejection.•Case study in chemical processes.
The purpose of this paper is to present the linear filtered positional generalized predictive controller (GPC) synthesis using both a positional process model and cost function to ensure stability and offset-free behavior (reference tracking and disturbance rejection), which involves selecting an integral polynomial weighting filter for the setpoint and output of the process, thereby extending the applicability of the predictive controllers to different reference shapes and step disturbances for handling chemical processes. Additionally, robustness aspects are incorporated into the control design of the weighting polynomials, an implementation which involves the filter tuning parameters using a multi-objective optimization based on genetic algorithm. Numerical simulations are conducted featuring two nonlinear chemical processes models (CSTR and boiler level) to assess the efficiency, stability and robustness of different reference shapes and load disturbance rejection.</description><identifier>ISSN: 0263-8762</identifier><identifier>EISSN: 1744-3563</identifier><identifier>DOI: 10.1016/j.cherd.2016.10.038</identifier><language>eng</language><publisher>Rugby: Elsevier B.V</publisher><subject>Behavior ; Boilers ; Chemical processes ; Computer simulation ; Controllers ; Design optimization ; Disturbance rejection ; Generalized predictive controller ; Genetic algorithm ; Genetic algorithms ; Mathematical models ; Multiple objective analysis ; Offset-free ; Optimization ; Polynomials ; Predictive control ; Reference tracking ; Rejection ; Robustness (mathematics) ; Stability analysis ; Studies ; Weighting</subject><ispartof>Chemical engineering research & design, 2017-01, Vol.117, p.265-273</ispartof><rights>2016 Institution of Chemical Engineers</rights><rights>Copyright Elsevier Science Ltd. Jan 2017</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c368t-90318db45163a2243cf0572043fd57be0dd14518538dbdbb4baf5f81328909e53</citedby><cites>FETCH-LOGICAL-c368t-90318db45163a2243cf0572043fd57be0dd14518538dbdbb4baf5f81328909e53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Araújo, Rejane de B.</creatorcontrib><creatorcontrib>Coelho, Antonio A.R.</creatorcontrib><title>Filtered predictive control design using multi-objective optimization based on genetic algorithm for handling offset in chemical processes</title><title>Chemical engineering research & design</title><description>[Display omitted]
•GPC using multi-objective optimization for handling offset in chemical processes.•Filtered predictive control to deal with offset in chemical processes (FP-GPC).•Multi-objective optimization based on genetic algorithm to find optimal tuning.•Handling reference tracking and disturbance rejection.•Case study in chemical processes.
The purpose of this paper is to present the linear filtered positional generalized predictive controller (GPC) synthesis using both a positional process model and cost function to ensure stability and offset-free behavior (reference tracking and disturbance rejection), which involves selecting an integral polynomial weighting filter for the setpoint and output of the process, thereby extending the applicability of the predictive controllers to different reference shapes and step disturbances for handling chemical processes. Additionally, robustness aspects are incorporated into the control design of the weighting polynomials, an implementation which involves the filter tuning parameters using a multi-objective optimization based on genetic algorithm. Numerical simulations are conducted featuring two nonlinear chemical processes models (CSTR and boiler level) to assess the efficiency, stability and robustness of different reference shapes and load disturbance rejection.</description><subject>Behavior</subject><subject>Boilers</subject><subject>Chemical processes</subject><subject>Computer simulation</subject><subject>Controllers</subject><subject>Design optimization</subject><subject>Disturbance rejection</subject><subject>Generalized predictive controller</subject><subject>Genetic algorithm</subject><subject>Genetic algorithms</subject><subject>Mathematical models</subject><subject>Multiple objective analysis</subject><subject>Offset-free</subject><subject>Optimization</subject><subject>Polynomials</subject><subject>Predictive control</subject><subject>Reference tracking</subject><subject>Rejection</subject><subject>Robustness (mathematics)</subject><subject>Stability analysis</subject><subject>Studies</subject><subject>Weighting</subject><issn>0263-8762</issn><issn>1744-3563</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp9kMuOEzEQRS0EEmHgC9hYYt3Bj-6Os2CBRsyANNJsYG257XJSrW472M5I8Al8NRXCejZ-VN261z6MvZdiK4UcP85bf4QStoouVNkKbV6wjdz1faeHUb9kG6FG3ZndqF6zN7XOQgjqmg37c4dLgwKBn2hB3_AJuM-plbzwABUPiZ8rpgNfz0vDLk8zXEX51HDF365hTnxylSzocIAEDT13yyEXbMeVx1z40aWwXExyjBUax8TpwSt6t1Bu9lAr1LfsVXRLhXf_9xv24-7L99uv3cPj_bfbzw-d16Np3V5oacLUD3LUTqle-yiGnRK9jmHYTSBCkNQ0gyZVmKZ-cnGIRmpl9mIPg75hH66-lPzzDLXZOZ9LokirhFJC9EZpUumrypdca4FoTwVXV35ZKewFup3tP-j2Av1SJOg09ek6BfSBJ4Riq0dIntAW4mZDxmfn_wLom46F</recordid><startdate>201701</startdate><enddate>201701</enddate><creator>Araújo, Rejane de B.</creator><creator>Coelho, Antonio A.R.</creator><general>Elsevier B.V</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8FD</scope><scope>JG9</scope></search><sort><creationdate>201701</creationdate><title>Filtered predictive control design using multi-objective optimization based on genetic algorithm for handling offset in chemical processes</title><author>Araújo, Rejane de B. ; Coelho, Antonio A.R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c368t-90318db45163a2243cf0572043fd57be0dd14518538dbdbb4baf5f81328909e53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Behavior</topic><topic>Boilers</topic><topic>Chemical processes</topic><topic>Computer simulation</topic><topic>Controllers</topic><topic>Design optimization</topic><topic>Disturbance rejection</topic><topic>Generalized predictive controller</topic><topic>Genetic algorithm</topic><topic>Genetic algorithms</topic><topic>Mathematical models</topic><topic>Multiple objective analysis</topic><topic>Offset-free</topic><topic>Optimization</topic><topic>Polynomials</topic><topic>Predictive control</topic><topic>Reference tracking</topic><topic>Rejection</topic><topic>Robustness (mathematics)</topic><topic>Stability analysis</topic><topic>Studies</topic><topic>Weighting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Araújo, Rejane de B.</creatorcontrib><creatorcontrib>Coelho, Antonio A.R.</creatorcontrib><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><jtitle>Chemical engineering research & design</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Araújo, Rejane de B.</au><au>Coelho, Antonio A.R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Filtered predictive control design using multi-objective optimization based on genetic algorithm for handling offset in chemical processes</atitle><jtitle>Chemical engineering research & design</jtitle><date>2017-01</date><risdate>2017</risdate><volume>117</volume><spage>265</spage><epage>273</epage><pages>265-273</pages><issn>0263-8762</issn><eissn>1744-3563</eissn><abstract>[Display omitted]
•GPC using multi-objective optimization for handling offset in chemical processes.•Filtered predictive control to deal with offset in chemical processes (FP-GPC).•Multi-objective optimization based on genetic algorithm to find optimal tuning.•Handling reference tracking and disturbance rejection.•Case study in chemical processes.
The purpose of this paper is to present the linear filtered positional generalized predictive controller (GPC) synthesis using both a positional process model and cost function to ensure stability and offset-free behavior (reference tracking and disturbance rejection), which involves selecting an integral polynomial weighting filter for the setpoint and output of the process, thereby extending the applicability of the predictive controllers to different reference shapes and step disturbances for handling chemical processes. Additionally, robustness aspects are incorporated into the control design of the weighting polynomials, an implementation which involves the filter tuning parameters using a multi-objective optimization based on genetic algorithm. Numerical simulations are conducted featuring two nonlinear chemical processes models (CSTR and boiler level) to assess the efficiency, stability and robustness of different reference shapes and load disturbance rejection.</abstract><cop>Rugby</cop><pub>Elsevier B.V</pub><doi>10.1016/j.cherd.2016.10.038</doi><tpages>9</tpages></addata></record> |
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subjects | Behavior Boilers Chemical processes Computer simulation Controllers Design optimization Disturbance rejection Generalized predictive controller Genetic algorithm Genetic algorithms Mathematical models Multiple objective analysis Offset-free Optimization Polynomials Predictive control Reference tracking Rejection Robustness (mathematics) Stability analysis Studies Weighting |
title | Filtered predictive control design using multi-objective optimization based on genetic algorithm for handling offset in chemical processes |
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