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Vector-based model predictive hysteresis current control for asynchronous motor

Vector-based model predictive hysteresis current control for asynchronous motor
Vector-based model predictive hysteresis current control for asynchronous motor

Model predictive control, especially model predictive current control, has received a great deal of attention in the motor drive field in recent years, due to its ability to render fast dynamic response and to handle multiple variables, nonlinearities, and system constraints in an intuitive way. However, the conventional single-vector-based model predictive hysteresis current control (SV-MPHCC) brings about some problems, such as high sampling frequency and poor steady-state control performance. In this paper' a novel double-vector-based model predictive hysteresis current control (DV-MPHCC), which utilizes an arbitrary vector and a zero vector to form a voltage vector combination, is proposed. To verify the improved performance over a short predictive horizon, a series of comparisons are conducted between the two control algorithms by simulation and experiment in the paper. Both simulation and experiment results validate that the DV-MPHCC proposed has advanced steady-state control performance, and a lower sampling frequency.

Asynchronous motor, double-vector, Hysteresis, Hysteresis motors, Mathematical model, model predictive hysteresis current control, Predictive models, sampling frequency, single-vector, Stators, Torque
0278-0046
Xue, Yaru
b4b2cffd-fb1e-4f75-9ba8-d19b74a3c16d
Meng, Dongyi
ba474ea2-0304-45d4-8201-f2cbe3ee0006
Yin, Shaobo
cb6dbcc0-ec34-41fe-b8cb-bc7e1e2c72a0
Han, Wei
83b53c96-8755-431b-a304-de76d42834a7
Yan, Xingda
2d256fbf-9bee-4c5e-9d75-fe15d1a96ade
Shu, Zhan
ea5dc18c-d375-4db0-bbcc-dd0229f3a1cb
Diao, Lijun
4129d449-8ec9-49ab-8438-978e67713ab9
Xue, Yaru
b4b2cffd-fb1e-4f75-9ba8-d19b74a3c16d
Meng, Dongyi
ba474ea2-0304-45d4-8201-f2cbe3ee0006
Yin, Shaobo
cb6dbcc0-ec34-41fe-b8cb-bc7e1e2c72a0
Han, Wei
83b53c96-8755-431b-a304-de76d42834a7
Yan, Xingda
2d256fbf-9bee-4c5e-9d75-fe15d1a96ade
Shu, Zhan
ea5dc18c-d375-4db0-bbcc-dd0229f3a1cb
Diao, Lijun
4129d449-8ec9-49ab-8438-978e67713ab9

Xue, Yaru, Meng, Dongyi, Yin, Shaobo, Han, Wei, Yan, Xingda, Shu, Zhan and Diao, Lijun (2019) Vector-based model predictive hysteresis current control for asynchronous motor. IEEE Transactions on Industrial Electronics. (doi:10.1109/TIE.2018.2886754).

Record type: Article

Abstract

Model predictive control, especially model predictive current control, has received a great deal of attention in the motor drive field in recent years, due to its ability to render fast dynamic response and to handle multiple variables, nonlinearities, and system constraints in an intuitive way. However, the conventional single-vector-based model predictive hysteresis current control (SV-MPHCC) brings about some problems, such as high sampling frequency and poor steady-state control performance. In this paper' a novel double-vector-based model predictive hysteresis current control (DV-MPHCC), which utilizes an arbitrary vector and a zero vector to form a voltage vector combination, is proposed. To verify the improved performance over a short predictive horizon, a series of comparisons are conducted between the two control algorithms by simulation and experiment in the paper. Both simulation and experiment results validate that the DV-MPHCC proposed has advanced steady-state control performance, and a lower sampling frequency.

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More information

Accepted/In Press date: 29 November 2018
e-pub ahead of print date: 7 January 2019
Keywords: Asynchronous motor, double-vector, Hysteresis, Hysteresis motors, Mathematical model, model predictive hysteresis current control, Predictive models, sampling frequency, single-vector, Stators, Torque

Identifiers

Local EPrints ID: 430144
URI: http://eprints.soton.ac.uk/id/eprint/430144
ISSN: 0278-0046
PURE UUID: f64250de-76f8-440d-9ff9-15ea437ce66f
ORCID for Zhan Shu: ORCID iD orcid.org/0000-0002-5933-254X

Catalogue record

Date deposited: 12 Apr 2019 16:30
Last modified: 26 Nov 2021 02:58

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Contributors

Author: Yaru Xue
Author: Dongyi Meng
Author: Shaobo Yin
Author: Wei Han
Author: Xingda Yan
Author: Zhan Shu ORCID iD
Author: Lijun Diao

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