@inproceedings{f8072cd382384464b6ca5bcda0690330,
title = "Energy-saving variable bias current optimization for magnetic bearing using adaptive differential evolution",
abstract = "This study proposes an adaptive differential evolution (ADE)-based variable bias current control strategy to improve the energy efficiency of an active magnetic bearing (AMB) system. In the AMB system, the drive current is composed of a control current and a superimposed bias current in which the former is controlled by an external controller used to regulate the rotor position while the latter is set as a pre-designed constant used to improve the linearity and dynamic performance. Generally, the bias current causes power loss even if no force is required. In this regard, the ADE-based variable bias current control strategy is proposed to minimize the energy consumption of the AMB control system without altering the control performance. Experimental results demonstrate the high-accuracy control and significant energy saving performances of the proposed method. The energy improvements compared to baseline were 20.24% and 17.65% for the operation periods of 10 s and 50 s, respectively.",
keywords = "Differential evolution, Energy-saving, Magnetic bearing, Variable bias current optimization",
author = "Chen, {Syuan Yi} and Song, {Min Han}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 8th International Conference on Swarm Intelligence, ICSI 2017 ; Conference date: 27-07-2017 Through 01-08-2017",
year = "2017",
doi = "10.1007/978-3-319-61824-1_51",
language = "English",
isbn = "9783319618234",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "466--474",
editor = "Ying Tan and Hideyuki Takagi and Yuhui Shi",
booktitle = "Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings",
}
