Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/219420
Title: Robust Predictive Current Control With Online Disturbance Estimation for Induction Machine Drives
Authors: Bo Wang;Xianle Chen;Yong Yu;Gaolin Wang;Dianguo Xu
Year: 2017
Publisher: IEEE
Abstract: This paper presents robust predictive current control (RPCC) with online disturbance estimation to achieve high- performance current loop for induction machine (IM) drives. The disturbance caused by parameter variations and other unmodeled dynamics is considered in modeling of IM. Based on this model, the control law of the proposed scheme is derived, where a discrete Luenberger observer is designed to estimate the future values of stator current and disturbance. The selection of the designed observer gain is a compromise between the system control bandwidth and robustness. Compared with the conventional RPCC, the proposed scheme removes the steady-state current error caused by the system disturbance. Experimental results on an industrial induction machine drive show that the proposed scheme provides fast and static-errorless current tracking even with mismatched machine parameters.
URI: http://localhost/handle/Hannan/219420
volume: 32
issue: 6
More Information: 4663,
4674
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7552585.pdf2.47 MBAdobe PDF
Title: Robust Predictive Current Control With Online Disturbance Estimation for Induction Machine Drives
Authors: Bo Wang;Xianle Chen;Yong Yu;Gaolin Wang;Dianguo Xu
Year: 2017
Publisher: IEEE
Abstract: This paper presents robust predictive current control (RPCC) with online disturbance estimation to achieve high- performance current loop for induction machine (IM) drives. The disturbance caused by parameter variations and other unmodeled dynamics is considered in modeling of IM. Based on this model, the control law of the proposed scheme is derived, where a discrete Luenberger observer is designed to estimate the future values of stator current and disturbance. The selection of the designed observer gain is a compromise between the system control bandwidth and robustness. Compared with the conventional RPCC, the proposed scheme removes the steady-state current error caused by the system disturbance. Experimental results on an industrial induction machine drive show that the proposed scheme provides fast and static-errorless current tracking even with mismatched machine parameters.
URI: http://localhost/handle/Hannan/219420
volume: 32
issue: 6
More Information: 4663,
4674
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7552585.pdf2.47 MBAdobe PDF
Title: Robust Predictive Current Control With Online Disturbance Estimation for Induction Machine Drives
Authors: Bo Wang;Xianle Chen;Yong Yu;Gaolin Wang;Dianguo Xu
Year: 2017
Publisher: IEEE
Abstract: This paper presents robust predictive current control (RPCC) with online disturbance estimation to achieve high- performance current loop for induction machine (IM) drives. The disturbance caused by parameter variations and other unmodeled dynamics is considered in modeling of IM. Based on this model, the control law of the proposed scheme is derived, where a discrete Luenberger observer is designed to estimate the future values of stator current and disturbance. The selection of the designed observer gain is a compromise between the system control bandwidth and robustness. Compared with the conventional RPCC, the proposed scheme removes the steady-state current error caused by the system disturbance. Experimental results on an industrial induction machine drive show that the proposed scheme provides fast and static-errorless current tracking even with mismatched machine parameters.
URI: http://localhost/handle/Hannan/219420
volume: 32
issue: 6
More Information: 4663,
4674
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7552585.pdf2.47 MBAdobe PDF