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Direct Torque Control of Sensorless Induction Machine Drives: A Two-Stage Kalman Filter Approach
Extended Kalman filter (EKF) has been widely applied for sensorless direct torque control (DTC) in induction machines (IMs). One key problem associated with EKF is that the estimator suffers from computational burden and numerical problems resulting from high order mathematical models. To reduce the...
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| Published in: | Mathematical problems in engineering 2015-01, Vol.2015 (2015), p.1-17 |
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| container_end_page | 17 |
| container_issue | 2015 |
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| container_title | Mathematical problems in engineering |
| container_volume | 2015 |
| creator | Yi, Boyu Chen, Lingyu Kang, Longyun Zhang, Jinliang Xu, Zhihui |
| description | Extended Kalman filter (EKF) has been widely applied for sensorless direct torque control (DTC) in induction machines (IMs). One key problem associated with EKF is that the estimator suffers from computational burden and numerical problems resulting from high order mathematical models. To reduce the computational cost, a two-stage extended Kalman filter (TEKF) based solution is presented for closed-loop stator flux, speed, and torque estimation of IM to achieve sensorless DTC-SVM operations in this paper. The novel observer can be similarly derived as the optimal two-stage Kalman filter (TKF) which has been proposed by several researchers. Compared to a straightforward implementation of a conventional EKF, the TEKF estimator can reduce the number of arithmetic operations. Simulation and experimental results verify the performance of the proposed TEKF estimator for DTC of IMs. |
| doi_str_mv | 10.1155/2015/609586 |
| format | article |
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One key problem associated with EKF is that the estimator suffers from computational burden and numerical problems resulting from high order mathematical models. To reduce the computational cost, a two-stage extended Kalman filter (TEKF) based solution is presented for closed-loop stator flux, speed, and torque estimation of IM to achieve sensorless DTC-SVM operations in this paper. The novel observer can be similarly derived as the optimal two-stage Kalman filter (TKF) which has been proposed by several researchers. Compared to a straightforward implementation of a conventional EKF, the TEKF estimator can reduce the number of arithmetic operations. Simulation and experimental results verify the performance of the proposed TEKF estimator for DTC of IMs.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2015/609586</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Closed loops ; Computational efficiency ; Computing costs ; Estimators ; Extended Kalman filter ; Induction motors ; Kalman filters ; Mathematical models ; Motors ; Neural networks ; Optimization ; Parameter estimation ; Torque ; Velocity</subject><ispartof>Mathematical problems in engineering, 2015-01, Vol.2015 (2015), p.1-17</ispartof><rights>Copyright © 2015 Jinliang Zhang et al.</rights><rights>Copyright © 2015 Jinliang Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c347t-6bc2f32dc1f45049e8802f97746ef10f50b14295970b0e518d8cba907a49ece73</cites><orcidid>0000-0002-9479-6403 ; 0000-0001-8315-9276</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1721312055/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1721312055?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,777,781,25734,27905,27906,36993,36994,44571,74875</link.rule.ids></links><search><contributor>Djemai, Mohamed</contributor><creatorcontrib>Yi, Boyu</creatorcontrib><creatorcontrib>Chen, Lingyu</creatorcontrib><creatorcontrib>Kang, Longyun</creatorcontrib><creatorcontrib>Zhang, Jinliang</creatorcontrib><creatorcontrib>Xu, Zhihui</creatorcontrib><title>Direct Torque Control of Sensorless Induction Machine Drives: A Two-Stage Kalman Filter Approach</title><title>Mathematical problems in engineering</title><description>Extended Kalman filter (EKF) has been widely applied for sensorless direct torque control (DTC) in induction machines (IMs). 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One key problem associated with EKF is that the estimator suffers from computational burden and numerical problems resulting from high order mathematical models. To reduce the computational cost, a two-stage extended Kalman filter (TEKF) based solution is presented for closed-loop stator flux, speed, and torque estimation of IM to achieve sensorless DTC-SVM operations in this paper. The novel observer can be similarly derived as the optimal two-stage Kalman filter (TKF) which has been proposed by several researchers. Compared to a straightforward implementation of a conventional EKF, the TEKF estimator can reduce the number of arithmetic operations. 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| source | Open Access: Wiley-Blackwell Open Access Journals; Publicly Available Content Database; IngentaConnect Journals |
| subjects | Closed loops Computational efficiency Computing costs Estimators Extended Kalman filter Induction motors Kalman filters Mathematical models Motors Neural networks Optimization Parameter estimation Torque Velocity |
| title | Direct Torque Control of Sensorless Induction Machine Drives: A Two-Stage Kalman Filter Approach |
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