@inproceedings{33858174d3d94f73a788ff93bb07ca1b,
title = "New model predictive control for improved disturbance rejection",
abstract = "In industrial systems, measurable but controllable disturbances are common and may drive systems away from their references. In standard MPC, its output prediction will have large errors due to these disturbances and thus cause poor regulation performance. In this paper, a complete plant model with disturbance dynamics is considered for better output prediction in MPC so as to improve its regulation. Since unknown future disturbances are also involved, they can be predicted from their past values. To verify its efficiency, a permanent magnet synchronous motor is simulated and shows significant improvement in disturbance rejection in comparison to the integral MPC and classical feedforward control.",
keywords = "Disturbance prediction, MPC, SQP, regulation control",
author = "Xian Li and Shuai Liu and Tan, {Kok Kiong} and Wang, {Qing Guo}",
note = "Publisher Copyright: {\textcopyright} 2016 TCCT.; 35th Chinese Control Conference, CCC 2016 ; Conference date: 27-07-2016 Through 29-07-2016",
year = "2016",
month = aug,
day = "26",
doi = "10.1109/ChiCC.2016.7554023",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "4318--4323",
editor = "Jie Chen and Qianchuan Zhao and Jie Chen",
booktitle = "Proceedings of the 35th Chinese Control Conference, CCC 2016",
address = "United States",
}