Grey Wolf Optimizer Algorithm-Based Tuning of Fuzzy Control Systems With Reduced Parametric Sensitivity
This paper proposes an innovative tuning approach for fuzzy control systems (CSs) with a reduced parametric sensitivity using the Grey Wolf Optimizer (GWO) algorithm. The CSs consist of servo system processes controlled by Takagi-Sugeno-Kang proportional-integral fuzzy controllers (TSK PI-FCs). The...
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| Veröffentlicht in: | IEEE transactions on industrial electronics (1982) 2017-01, Vol.64 (1), p.527-534 |
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| container_title | IEEE transactions on industrial electronics (1982) |
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| creator | Precup, Radu-Emil David, Radu-Codrut Petriu, Emil M. |
| description | This paper proposes an innovative tuning approach for fuzzy control systems (CSs) with a reduced parametric sensitivity using the Grey Wolf Optimizer (GWO) algorithm. The CSs consist of servo system processes controlled by Takagi-Sugeno-Kang proportional-integral fuzzy controllers (TSK PI-FCs). The process models have second-order dynamics with an integral component, variable parameters, a saturation, and dead-zone static nonlinearity. The sensitivity analysis employs output sensitivity functions of the sensitivity models defined with respect to the parametric variations of the processes. The GWO algorithm is used in solving the optimization problems, where the objective functions include the output sensitivity functions. GWO's motivation is based on its low-computational cost. The tuning approach is validated in an experimental case study of a position control for a laboratory nonlinear servo system, and TSK PI-FCs with a reduced process small time constant sensitivity are offered. |
| doi_str_mv | 10.1109/TIE.2016.2607698 |
| format | Article |
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The CSs consist of servo system processes controlled by Takagi-Sugeno-Kang proportional-integral fuzzy controllers (TSK PI-FCs). The process models have second-order dynamics with an integral component, variable parameters, a saturation, and dead-zone static nonlinearity. The sensitivity analysis employs output sensitivity functions of the sensitivity models defined with respect to the parametric variations of the processes. The GWO algorithm is used in solving the optimization problems, where the objective functions include the output sensitivity functions. GWO's motivation is based on its low-computational cost. The tuning approach is validated in an experimental case study of a position control for a laboratory nonlinear servo system, and TSK PI-FCs with a reduced process small time constant sensitivity are offered.</description><identifier>ISSN: 0278-0046</identifier><identifier>EISSN: 1557-9948</identifier><identifier>DOI: 10.1109/TIE.2016.2607698</identifier><identifier>CODEN: ITIED6</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Control systems ; Experimental results ; Fuzzy control ; fuzzy control systems (CSs) ; Fuzzy systems ; Grey Wolf Optimizer (GWO) ; Heuristic algorithms ; Nonlinearity ; Optimization ; Parameter sensitivity ; parametric sensitivity ; Process control ; Proportional integral ; Sensitivity ; Sensitivity analysis ; servo systems ; Servocontrol ; Servomotors ; Time constant ; Tuning</subject><ispartof>IEEE transactions on industrial electronics (1982), 2017-01, Vol.64 (1), p.527-534</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2017</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c361t-ea8e4e62f4a44c046b82c7aa719f337aeace329dc04d27a8178b0e5f24114ef03</citedby><cites>FETCH-LOGICAL-c361t-ea8e4e62f4a44c046b82c7aa719f337aeace329dc04d27a8178b0e5f24114ef03</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7563386$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27903,27904,54736</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7563386$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Precup, Radu-Emil</creatorcontrib><creatorcontrib>David, Radu-Codrut</creatorcontrib><creatorcontrib>Petriu, Emil M.</creatorcontrib><title>Grey Wolf Optimizer Algorithm-Based Tuning of Fuzzy Control Systems With Reduced Parametric Sensitivity</title><title>IEEE transactions on industrial electronics (1982)</title><addtitle>TIE</addtitle><description>This paper proposes an innovative tuning approach for fuzzy control systems (CSs) with a reduced parametric sensitivity using the Grey Wolf Optimizer (GWO) algorithm. The CSs consist of servo system processes controlled by Takagi-Sugeno-Kang proportional-integral fuzzy controllers (TSK PI-FCs). The process models have second-order dynamics with an integral component, variable parameters, a saturation, and dead-zone static nonlinearity. The sensitivity analysis employs output sensitivity functions of the sensitivity models defined with respect to the parametric variations of the processes. The GWO algorithm is used in solving the optimization problems, where the objective functions include the output sensitivity functions. GWO's motivation is based on its low-computational cost. The tuning approach is validated in an experimental case study of a position control for a laboratory nonlinear servo system, and TSK PI-FCs with a reduced process small time constant sensitivity are offered.</description><subject>Algorithms</subject><subject>Control systems</subject><subject>Experimental results</subject><subject>Fuzzy control</subject><subject>fuzzy control systems (CSs)</subject><subject>Fuzzy systems</subject><subject>Grey Wolf Optimizer (GWO)</subject><subject>Heuristic algorithms</subject><subject>Nonlinearity</subject><subject>Optimization</subject><subject>Parameter sensitivity</subject><subject>parametric sensitivity</subject><subject>Process control</subject><subject>Proportional integral</subject><subject>Sensitivity</subject><subject>Sensitivity analysis</subject><subject>servo systems</subject><subject>Servocontrol</subject><subject>Servomotors</subject><subject>Time constant</subject><subject>Tuning</subject><issn>0278-0046</issn><issn>1557-9948</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kMFLwzAUh4MoOKd3wUvAc2eSpkl6nGObg8HETXYsWfs6M9pmJqnQ_fV2TDy9w_t-v_f4EHqkZEQpSV82i-mIESpGTBApUnWFBjRJZJSmXF2jAWFSRYRwcYvuvD8QQnlCkwHazx10eGurEq-OwdTmBA6Pq711JnzV0av2UOBN25hmj22JZ-3p1OGJbYKzFV53PkDt8bZn8QcUbd7D79rpGoIzOV5D400wPyZ09-im1JWHh785RJ-z6WbyFi1X88VkvIzyWNAQgVbAQbCSa87z_t2dYrnUWtK0jGOpQecQs7ToVwWTWlGpdgSSknFKOZQkHqLnS-_R2e8WfMgOtnVNfzKjiismRSJFT5ELlTvrvYMyOzpTa9dllGRnnVmvMzvrzP509pGnS8QAwD8uExHHSsS_S6hx_Q</recordid><startdate>20170101</startdate><enddate>20170101</enddate><creator>Precup, Radu-Emil</creator><creator>David, Radu-Codrut</creator><creator>Petriu, Emil M.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope></search><sort><creationdate>20170101</creationdate><title>Grey Wolf Optimizer Algorithm-Based Tuning of Fuzzy Control Systems With Reduced Parametric Sensitivity</title><author>Precup, Radu-Emil ; David, Radu-Codrut ; Petriu, Emil M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c361t-ea8e4e62f4a44c046b82c7aa719f337aeace329dc04d27a8178b0e5f24114ef03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Algorithms</topic><topic>Control systems</topic><topic>Experimental results</topic><topic>Fuzzy control</topic><topic>fuzzy control systems (CSs)</topic><topic>Fuzzy systems</topic><topic>Grey Wolf Optimizer (GWO)</topic><topic>Heuristic algorithms</topic><topic>Nonlinearity</topic><topic>Optimization</topic><topic>Parameter sensitivity</topic><topic>parametric sensitivity</topic><topic>Process control</topic><topic>Proportional integral</topic><topic>Sensitivity</topic><topic>Sensitivity analysis</topic><topic>servo systems</topic><topic>Servocontrol</topic><topic>Servomotors</topic><topic>Time constant</topic><topic>Tuning</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Precup, Radu-Emil</creatorcontrib><creatorcontrib>David, Radu-Codrut</creatorcontrib><creatorcontrib>Petriu, Emil M.</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><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on industrial electronics (1982)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Precup, Radu-Emil</au><au>David, Radu-Codrut</au><au>Petriu, Emil M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Grey Wolf Optimizer Algorithm-Based Tuning of Fuzzy Control Systems With Reduced Parametric Sensitivity</atitle><jtitle>IEEE transactions on industrial electronics (1982)</jtitle><stitle>TIE</stitle><date>2017-01-01</date><risdate>2017</risdate><volume>64</volume><issue>1</issue><spage>527</spage><epage>534</epage><pages>527-534</pages><issn>0278-0046</issn><eissn>1557-9948</eissn><coden>ITIED6</coden><abstract>This paper proposes an innovative tuning approach for fuzzy control systems (CSs) with a reduced parametric sensitivity using the Grey Wolf Optimizer (GWO) algorithm. The CSs consist of servo system processes controlled by Takagi-Sugeno-Kang proportional-integral fuzzy controllers (TSK PI-FCs). The process models have second-order dynamics with an integral component, variable parameters, a saturation, and dead-zone static nonlinearity. The sensitivity analysis employs output sensitivity functions of the sensitivity models defined with respect to the parametric variations of the processes. The GWO algorithm is used in solving the optimization problems, where the objective functions include the output sensitivity functions. GWO's motivation is based on its low-computational cost. The tuning approach is validated in an experimental case study of a position control for a laboratory nonlinear servo system, and TSK PI-FCs with a reduced process small time constant sensitivity are offered.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TIE.2016.2607698</doi><tpages>8</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Control systems Experimental results Fuzzy control fuzzy control systems (CSs) Fuzzy systems Grey Wolf Optimizer (GWO) Heuristic algorithms Nonlinearity Optimization Parameter sensitivity parametric sensitivity Process control Proportional integral Sensitivity Sensitivity analysis servo systems Servocontrol Servomotors Time constant Tuning |
| title | Grey Wolf Optimizer Algorithm-Based Tuning of Fuzzy Control Systems With Reduced Parametric Sensitivity |
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