HGO and neural network based integral sliding mode control for PMSMs with uncertainty

This paper proposes an integral sliding mode control that integrates a high-gain observer (HGO) and a radial basis function neural network (RBFNN) for a permanent magnet synchronous motor (PMSM) with uncertainty. Since the second-order motion equation of the PMSM is used to improve the control perfo...

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Vydané v:	JOURNAL OF POWER ELECTRONICS Ročník 20; číslo 5; s. 1206 - 1221
Hlavní autori:	Ge, Yang, Yang, Lihui, Ma, Xikui
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Singapore Springer Singapore 01.09.2020 Springer Nature B.V 전력전자학회
Predmet:	Accuracy Algorithms Controllers Design Electrical Machines and Networks Engineering Equations of motion High gain Neural networks Original Article Permanent magnets Power Electronics Radial basis function Sliding mode control Synchronous motors Systems stability Uncertainty 전기공학 Integral sliding mode control Parameter adaptive algorithm Permanent magnet synchronous motor Radial basis function neural network High-gain observer
ISSN:	1598-2092, 2093-4718
On-line prístup:	Získať plný text
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