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Self-Learning MTPA Control of Interior Permanent-Magnet Synchronous Machine Drives Based on Virtual Signal Injection   
Yazarlar
Tianfu Sun
Jiabin Wang
Doç. Dr. Mikail KOÇ
Türkiye
Xiao Chen
Özet
This paper describes a simple but effective novel self-learning maximum torque per ampere (MTPA) control scheme for interior permanent-magnet synchronous machine (IPMSM) drives to achieve fast dynamic response in tracking the MTPA points without accurate prior knowledge of machine parameters. The proposed self-learning control (SLC) scheme generates the optimal d-axis current command for MTPA operation after training. Virtual signal injection control (VSIC), which has been recently developed as a novel parameter-independent MTPA points tracking scheme, is utilized to train the SLC and compensate the error of the SLC during its operation. In this way, the proposed SLC can achieve the MTPA operation accurately with fast response and the online training of the SLC will not affect MTPA operation of IPMSM drives. The proposed control scheme is verified by simulations and experiments under various operation conditions on a prototype IPMSM drive system.
Anahtar Kelimeler
Interior permanent-magnet synchronous machine (IPMSM), maximum torque per ampere (MTPA) control, self-learning control (SLC), signal injection, signal processing, torque control, virtual signal injection control (VSIC)
Makale Türü Özgün Makale
Makale Alt Türü SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale
Dergi Adı IEEE Transactions on Industry Applications
Dergi ISSN 0093-9994
Dergi Tarandığı Indeksler SCI-Expanded
Dergi Grubu Q1
Makale Dili İngilizce
Basım Tarihi 07-2016
Cilt No 52
Sayı 4
Sayfalar 3062 / 3070
Doi Numarası 10.1109/TIA.2016.2533601
Makale Linki http://ieeexplore.ieee.org/document/7416647/