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
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Popis
Shrnutí: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 performance, the speed derivative, which cannot be measured directly, is required. Thus, the HGO is designed to estimate the unknown state (speed derivative). In addition, the RBFNN is designed to approximate the compounded disturbance including the lumped disturbance of system and the HGO error effect. Unlike previous studies, the output of the RBFNN is compensated by both the controller and the HGO to improve the system robustness and observer accuracy. The sliding function and the HGO error are both taken into account in the RBFNN to explicitly guarantee the stability of the whole system. To demonstrate the superiority of the proposed method, comparative simulations and experiments were carried out in different cases.
Bibliografia:ObjectType-Article-1
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content type line 14
https://link.springer.com/article/10.1007/s43236-020-00111-w
ISSN:1598-2092
2093-4718
DOI:10.1007/s43236-020-00111-w