Self-Learning MTPA Control of Interior Permanent-Magnet Synchronous Machine Drives Based on Virtual Signal Injection
   
Yazarlar (4)
Tianfu Sun The University Of Sheffield, İngiltere
Jiabin Wang The University Of Sheffield, İngiltere
Doç. Dr. Mikail KOÇ The University Of Sheffield, İngiltere
Xiao Chen The University Of Sheffield, İngiltere
Makale Türü Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
Dergi Adı IEEE Transactions on Industry Applications (Q1)
Dergi ISSN 0093-9994 Wos Dergi Scopus Dergi
Dergi Tarandığı Indeksler SCI-Expanded
Makale Dili İngilizce Basım Tarihi 01-2015
Cilt / Sayı / Sayfa 52 / 4 / 3062–3070 DOI 10.1109/TIA.2016.2533601
Makale Linki http://ieeexplore.ieee.org/document/7416647/
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
Curve fitting | Maximum torque per ampere control (MTPA) | Permanent magnet synchronous machine (IPMSM) | Recursive least square (RLS) | Self-learning control (SLC) | Signal injection | Signal processing | Torque control | Virtual signal injection (VSIC)