Linear parameter-varying techniques for control of a magnetic bearing system

Bei Lu; Heeju Choi; Gregory Buckner; Kari Tammi

doi:10.1016/j.conengprac.2008.01.002

Linear parameter-varying techniques for control of a magnetic bearing system

Bei Lu (Corresponding Author), Heeju Choi, Gregory Buckner, Kari Tammi

Research output: Contribution to journal › Article › Scientific › peer-review

51 Citations (Scopus)

Abstract

In this paper, a linear parameter-varying (LPV) control design method is evaluated experimentally on an active magnetic bearing (AMB) system. A speed-dependent LPV model of the AMB system is derived. Model uncertainties are identified using artificial neural networks, and an uncertainty weighting function is approximated for LPV control synthesis. Experiments are conducted to verify the robustness of LPV controllers for a wide range of rotational speed. This LPV control approach eliminates the need for gain-scheduling, and provides better performance than the traditional proportional-integral-derivative control for high-speed operation.

Original language	English
Pages (from-to)	1161 - 1172
Number of pages	12
Journal	Control Engineering Practice
Volume	16
DOIs	https://doi.org/10.1016/j.conengprac.2008.01.002
Publication status	Published - 2008
MoE publication type	A1 Journal article-refereed

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Lu, B., Choi, H., Buckner, G., & Tammi, K. (2008). Linear parameter-varying techniques for control of a magnetic bearing system. Control Engineering Practice, 16, 1161 - 1172. https://doi.org/10.1016/j.conengprac.2008.01.002

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abstract = "In this paper, a linear parameter-varying (LPV) control design method is evaluated experimentally on an active magnetic bearing (AMB) system. A speed-dependent LPV model of the AMB system is derived. Model uncertainties are identified using artificial neural networks, and an uncertainty weighting function is approximated for LPV control synthesis. Experiments are conducted to verify the robustness of LPV controllers for a wide range of rotational speed. This LPV control approach eliminates the need for gain-scheduling, and provides better performance than the traditional proportional-integral-derivative control for high-speed operation.",

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AB - In this paper, a linear parameter-varying (LPV) control design method is evaluated experimentally on an active magnetic bearing (AMB) system. A speed-dependent LPV model of the AMB system is derived. Model uncertainties are identified using artificial neural networks, and an uncertainty weighting function is approximated for LPV control synthesis. Experiments are conducted to verify the robustness of LPV controllers for a wide range of rotational speed. This LPV control approach eliminates the need for gain-scheduling, and provides better performance than the traditional proportional-integral-derivative control for high-speed operation.

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