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Energy-Saving Variable Bias Current Optimization for Magnetic Bearing Using Adaptive Differential Evolution

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10385))

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.

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Correspondence to Syuan-Yi Chen .

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Chen, SY., Song, MH. (2017). Energy-Saving Variable Bias Current Optimization for Magnetic Bearing Using Adaptive Differential Evolution. In: Tan, Y., Takagi, H., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10385. Springer, Cham. https://doi.org/10.1007/978-3-319-61824-1_51

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  • DOI: https://doi.org/10.1007/978-3-319-61824-1_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61823-4

  • Online ISBN: 978-3-319-61824-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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