Kriging-assisted robust black-box simulation optimization in direct speed control of DC motor under uncertainty
In spite of the wide improvement in computer simulation packages, analyzing, and optimizing the simulation model, particularly under uncertainty can still be computationally expensive and time-consuming. This paper aims to tackle these features by proposing a comprehensive methodology applied to bla...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2018
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| Online Access: | http://psasir.upm.edu.my/id/eprint/74807/1/Kriging-assisted%20robust%20black-box%20simulation%20optimization%20in%20direct%20speed%20control%20of%20DC%20motor%20under%20uncertainty.pdf |
| _version_ | 1796979765283913728 |
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| author | Parnianifard, Amir Ahmad, Siti Azfanizam Mohd Ariffin, Mohd Khairol Anuar Ismail, Mohd Idris Shah |
| author_facet | Parnianifard, Amir Ahmad, Siti Azfanizam Mohd Ariffin, Mohd Khairol Anuar Ismail, Mohd Idris Shah |
| author_sort | Parnianifard, Amir |
| collection | UPM |
| description | In spite of the wide improvement in computer simulation packages, analyzing, and optimizing the simulation model, particularly under uncertainty can still be computationally expensive and time-consuming. This paper aims to tackle these features by proposing a comprehensive methodology applied to black-box stochastic simulation models under uncertainty. For this purpose, the common surrogate model as Kriging metamodel is served to fit the simulation input-output data produced by Latin hypercube sampling experimental design. Taguchi terminology of robust design enables the optimization methods to control uncertainty and uncontrollable environmental factors. So as to formulate robust counterpart optimization, three different models in the class of dual-response surface are integrated with metamodel and robust design. Leave-one-out cross-validation is applied to validate the Kriging metamodel. Finally, a numerical case study as a direct speed control of dc motor under uncertainty is served to demonstrate the applicability of the proposed method in real engineering problems. This simplified and practical mechatronics case illustrates how the proposed procedure can be expanded for analyzing and optimizing the real complex systems. |
| first_indexed | 2024-03-06T10:13:43Z |
| format | Article |
| id | upm.eprints-74807 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T10:13:43Z |
| publishDate | 2018 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | upm.eprints-748072020-04-20T16:43:45Z http://psasir.upm.edu.my/id/eprint/74807/ Kriging-assisted robust black-box simulation optimization in direct speed control of DC motor under uncertainty Parnianifard, Amir Ahmad, Siti Azfanizam Mohd Ariffin, Mohd Khairol Anuar Ismail, Mohd Idris Shah In spite of the wide improvement in computer simulation packages, analyzing, and optimizing the simulation model, particularly under uncertainty can still be computationally expensive and time-consuming. This paper aims to tackle these features by proposing a comprehensive methodology applied to black-box stochastic simulation models under uncertainty. For this purpose, the common surrogate model as Kriging metamodel is served to fit the simulation input-output data produced by Latin hypercube sampling experimental design. Taguchi terminology of robust design enables the optimization methods to control uncertainty and uncontrollable environmental factors. So as to formulate robust counterpart optimization, three different models in the class of dual-response surface are integrated with metamodel and robust design. Leave-one-out cross-validation is applied to validate the Kriging metamodel. Finally, a numerical case study as a direct speed control of dc motor under uncertainty is served to demonstrate the applicability of the proposed method in real engineering problems. This simplified and practical mechatronics case illustrates how the proposed procedure can be expanded for analyzing and optimizing the real complex systems. Institute of Electrical and Electronics Engineers (IEEE) 2018 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/74807/1/Kriging-assisted%20robust%20black-box%20simulation%20optimization%20in%20direct%20speed%20control%20of%20DC%20motor%20under%20uncertainty.pdf Parnianifard, Amir and Ahmad, Siti Azfanizam and Mohd Ariffin, Mohd Khairol Anuar and Ismail, Mohd Idris Shah (2018) Kriging-assisted robust black-box simulation optimization in direct speed control of DC motor under uncertainty. IEEE Transactions on Magnetics, 54 (7). pp. 1-9. ISSN 0018-9464 10.1109/TMAG.2018.2829767 |
| spellingShingle | Parnianifard, Amir Ahmad, Siti Azfanizam Mohd Ariffin, Mohd Khairol Anuar Ismail, Mohd Idris Shah Kriging-assisted robust black-box simulation optimization in direct speed control of DC motor under uncertainty |
| title | Kriging-assisted robust black-box simulation optimization in direct speed control of DC motor under uncertainty |
| title_full | Kriging-assisted robust black-box simulation optimization in direct speed control of DC motor under uncertainty |
| title_fullStr | Kriging-assisted robust black-box simulation optimization in direct speed control of DC motor under uncertainty |
| title_full_unstemmed | Kriging-assisted robust black-box simulation optimization in direct speed control of DC motor under uncertainty |
| title_short | Kriging-assisted robust black-box simulation optimization in direct speed control of DC motor under uncertainty |
| title_sort | kriging assisted robust black box simulation optimization in direct speed control of dc motor under uncertainty |
| url | http://psasir.upm.edu.my/id/eprint/74807/1/Kriging-assisted%20robust%20black-box%20simulation%20optimization%20in%20direct%20speed%20control%20of%20DC%20motor%20under%20uncertainty.pdf |
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