TY - JOUR
T1 - Online Autotuning Technique for IPMSM Servo Drive by Intelligent Identification of Moment of Inertia
AU - Lin, Faa Jeng
AU - Chen, Shih Gang
AU - Li, Shuai
AU - Chou, Hsiao Tse
AU - Lin, Jyun Ru
N1 - Publisher Copyright:
© 2005-2012 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - In this article, a real-time moment of inertia identification technique using Petri probabilistic fuzzy neural network with an asymmetric membership function (PPFNN-AMF) for an interior permanent magnet synchronous motor (IPMSM) servo drive is proposed. The estimated moment of inertia will be used in the online design of an integral-proportional (IP) speed controller to achieve the gains autotuning of the IPMSM servo drive. In the proposed method, the dynamic analysis of a field-oriented control IPMSM servo drive system with an IP speed controller is constructed first. Then, a heuristic approach using the PPFNN-AMF is proposed for the real-time identification of the moment of inertia of the IPMSM servo drive system. Moreover, the network structure and the convergence analysis of the PPFNN-AMF are devised and derivated. Furthermore, an IPMSM servo drive based on a high-performance digital signal processor is developed. Finally, from the experimental results, the gains of the IP speed controller can be tuned online effectively at different operating conditions with robust control characteristics.
AB - In this article, a real-time moment of inertia identification technique using Petri probabilistic fuzzy neural network with an asymmetric membership function (PPFNN-AMF) for an interior permanent magnet synchronous motor (IPMSM) servo drive is proposed. The estimated moment of inertia will be used in the online design of an integral-proportional (IP) speed controller to achieve the gains autotuning of the IPMSM servo drive. In the proposed method, the dynamic analysis of a field-oriented control IPMSM servo drive system with an IP speed controller is constructed first. Then, a heuristic approach using the PPFNN-AMF is proposed for the real-time identification of the moment of inertia of the IPMSM servo drive system. Moreover, the network structure and the convergence analysis of the PPFNN-AMF are devised and derivated. Furthermore, an IPMSM servo drive based on a high-performance digital signal processor is developed. Finally, from the experimental results, the gains of the IP speed controller can be tuned online effectively at different operating conditions with robust control characteristics.
KW - Asymmetric membership function (AMF)
KW - Petri probabilistic fuzzy neural network (PPFNN)
KW - interior permanent magnet synchronous motor (IPMSM)
KW - online gain autotuning
UR - http://www.scopus.com/inward/record.url?scp=85092089736&partnerID=8YFLogxK
U2 - 10.1109/TII.2020.2965194
DO - 10.1109/TII.2020.2965194
M3 - 期刊論文
AN - SCOPUS:85092089736
SN - 1551-3203
VL - 16
SP - 7579
EP - 7590
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 12
M1 - 8954652
ER -