Sliding mode control with neural network for active magnetic bearing system

Conference paper (2019)

Authors

Z. Cao DC systems, Energy conversion & Storage - EEMCS , Southeast University
J. Dong DC systems, Energy conversion & Storage - EEMCS
F.M. Wani Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering
H. Polinder Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering
P. Bauer DC systems, Energy conversion & Storage - EEMCS
Fei Peng Southeast University
Yunkai Huang Southeast University
Research Group
DC systems, Energy conversion & Storage (EEMCS) (TU Delft)
Copyright: © 2019 Z. Cao, J. Dong, F.M. Wani, H. Polinder, P. Bauer, Fei Peng, Yunkai Huang
DOI: https://doi.org/10.1109/IECON.2019.8926808
More Info
expand_more

Published Date

2019

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Electrical Sustainable Energy

Research Group

DC systems, Energy conversion & Storage

Abstract

A novel controller design procedure is proposed for a 5-degree-of-freedom (DOF) active magnetic bearing (AMB) system, based on sliding mode control (SMC) and neural network (NN). The SMC is used to achieve high robustness and fast response while the NN can compensate unmodeled uncertainty and external disturbance by on-line tuning algorithm. The proposed controller is compared with the well-tuned PID controller by simulations. The simulation results show the superior performance of the proposed controller.

Files

IECON2019_magnetic_bearing_iec... (pdf)
(pdf | 3.2 Mb)
Unknown license

Download not available

Sliding_Mode_Control_with_Neur... (pdf)
(pdf | 3.21 Mb)
- Embargo expired in 09-06-2020
Unknown license