Neural network based torque ripple minimisation in a switched reluctance motor
This paper presents an artificial neural network (ANN) solution to torque ripple reduction in a switched reluctance motor. Magnetic saturation together with salient stator and rotor poles give rise to a highly nonlinear torque/current/angle characteristic. The approach in this paper allows the neura...
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| creator | O'Donovan, J.G. Roche, P.J. Kavanagh, R.C. Egan, M.G. Murphy, J.M.D. |
| description | This paper presents an artificial neural network (ANN) solution to torque ripple reduction in a switched reluctance motor. Magnetic saturation together with salient stator and rotor poles give rise to a highly nonlinear torque/current/angle characteristic. The approach in this paper allows the neural network to be used to its full potential, that is, learning the nonlinear flux linkage characteristic while also incorporating a priori analytical knowledge of the torque production mechanism of the machine. This combination of neuro-learning and analytical insight results in a greatly simplified controller. Simulation results are presented to illustrate the performance of the proposed technique. Experimental results based on a floating point DSP processor are included.< > |
| doi_str_mv | 10.1109/IECON.1994.397968 |
| format | Conference Proceeding |
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Magnetic saturation together with salient stator and rotor poles give rise to a highly nonlinear torque/current/angle characteristic. The approach in this paper allows the neural network to be used to its full potential, that is, learning the nonlinear flux linkage characteristic while also incorporating a priori analytical knowledge of the torque production mechanism of the machine. This combination of neuro-learning and analytical insight results in a greatly simplified controller. Simulation results are presented to illustrate the performance of the proposed technique. Experimental results based on a floating point DSP processor are included.< ></description><identifier>ISBN: 0780313283</identifier><identifier>ISBN: 9780780313286</identifier><identifier>DOI: 10.1109/IECON.1994.397968</identifier><language>eng</language><publisher>IEEE</publisher><subject>Artificial neural networks ; Couplings ; Machine learning ; Magnetic flux ; Neural networks ; Reluctance motors ; Rotors ; Saturation magnetization ; Stators ; Torque</subject><ispartof>Proceedings of IECON'94 - 20th Annual Conference of IEEE Industrial Electronics, 1994, Vol.2, p.1226-1231 vol.2</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/397968$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,4036,4037,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/397968$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>O'Donovan, J.G.</creatorcontrib><creatorcontrib>Roche, P.J.</creatorcontrib><creatorcontrib>Kavanagh, R.C.</creatorcontrib><creatorcontrib>Egan, M.G.</creatorcontrib><creatorcontrib>Murphy, J.M.D.</creatorcontrib><title>Neural network based torque ripple minimisation in a switched reluctance motor</title><title>Proceedings of IECON'94 - 20th Annual Conference of IEEE Industrial Electronics</title><addtitle>IECON</addtitle><description>This paper presents an artificial neural network (ANN) solution to torque ripple reduction in a switched reluctance motor. Magnetic saturation together with salient stator and rotor poles give rise to a highly nonlinear torque/current/angle characteristic. The approach in this paper allows the neural network to be used to its full potential, that is, learning the nonlinear flux linkage characteristic while also incorporating a priori analytical knowledge of the torque production mechanism of the machine. This combination of neuro-learning and analytical insight results in a greatly simplified controller. Simulation results are presented to illustrate the performance of the proposed technique. Experimental results based on a floating point DSP processor are included.< ></description><subject>Artificial neural networks</subject><subject>Couplings</subject><subject>Machine learning</subject><subject>Magnetic flux</subject><subject>Neural networks</subject><subject>Reluctance motors</subject><subject>Rotors</subject><subject>Saturation magnetization</subject><subject>Stators</subject><subject>Torque</subject><isbn>0780313283</isbn><isbn>9780780313286</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1994</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj81KxDAUhQMiqOM8gK7yAq1Jb9skSymjDgydja6H2_YGo_0zSRl8ewvj2ZzN9x04jD1IkUopzNN-Vx3rVBqTp2CUKfUVuxNKC5CQabhh2xC-xJq8kEroW1bXtHjs-UjxPPlv3mCgjsfJ_yzEvZvnnvjgRje4gNFNI3cjRx7OLrafK-ipX9qIY7tS02rds2uLfaDtf2_Yx8vuvXpLDsfXffV8SJwUeUwaRZ0QOpMKCrTGAlqREViwWQFWK7S6RF1KwK7QDVmrNBVdluctytZ2Bjbs8bLriOg0ezeg_z1dHsMfF5ROOg</recordid><startdate>1994</startdate><enddate>1994</enddate><creator>O'Donovan, J.G.</creator><creator>Roche, P.J.</creator><creator>Kavanagh, R.C.</creator><creator>Egan, M.G.</creator><creator>Murphy, J.M.D.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1994</creationdate><title>Neural network based torque ripple minimisation in a switched reluctance motor</title><author>O'Donovan, J.G. ; Roche, P.J. ; Kavanagh, R.C. ; Egan, M.G. ; Murphy, J.M.D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i104t-b7ed00821735af9f3af02e3f3f253f87af86a8613ad58beff78e5d244ca1cfd93</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1994</creationdate><topic>Artificial neural networks</topic><topic>Couplings</topic><topic>Machine learning</topic><topic>Magnetic flux</topic><topic>Neural networks</topic><topic>Reluctance motors</topic><topic>Rotors</topic><topic>Saturation magnetization</topic><topic>Stators</topic><topic>Torque</topic><toplevel>online_resources</toplevel><creatorcontrib>O'Donovan, J.G.</creatorcontrib><creatorcontrib>Roche, P.J.</creatorcontrib><creatorcontrib>Kavanagh, R.C.</creatorcontrib><creatorcontrib>Egan, M.G.</creatorcontrib><creatorcontrib>Murphy, J.M.D.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>O'Donovan, J.G.</au><au>Roche, P.J.</au><au>Kavanagh, R.C.</au><au>Egan, M.G.</au><au>Murphy, J.M.D.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Neural network based torque ripple minimisation in a switched reluctance motor</atitle><btitle>Proceedings of IECON'94 - 20th Annual Conference of IEEE Industrial Electronics</btitle><stitle>IECON</stitle><date>1994</date><risdate>1994</risdate><volume>2</volume><spage>1226</spage><epage>1231 vol.2</epage><pages>1226-1231 vol.2</pages><isbn>0780313283</isbn><isbn>9780780313286</isbn><abstract>This paper presents an artificial neural network (ANN) solution to torque ripple reduction in a switched reluctance motor. Magnetic saturation together with salient stator and rotor poles give rise to a highly nonlinear torque/current/angle characteristic. The approach in this paper allows the neural network to be used to its full potential, that is, learning the nonlinear flux linkage characteristic while also incorporating a priori analytical knowledge of the torque production mechanism of the machine. This combination of neuro-learning and analytical insight results in a greatly simplified controller. Simulation results are presented to illustrate the performance of the proposed technique. Experimental results based on a floating point DSP processor are included.< ></abstract><pub>IEEE</pub><doi>10.1109/IECON.1994.397968</doi></addata></record> |
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| identifier | ISBN: 0780313283 |
| ispartof | Proceedings of IECON'94 - 20th Annual Conference of IEEE Industrial Electronics, 1994, Vol.2, p.1226-1231 vol.2 |
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| language | eng |
| recordid | cdi_ieee_primary_397968 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks Couplings Machine learning Magnetic flux Neural networks Reluctance motors Rotors Saturation magnetization Stators Torque |
| title | Neural network based torque ripple minimisation in a switched reluctance motor |
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