Control of a magnetic flywheel by a fuzzy neural network algorithm

J.-W. Kim; D.-J. Xuan; Y.-B. Kim

doi:10.1117/12.664650

2 May 2006 Control of a magnetic flywheel by a fuzzy neural network algorithm

J.-W. Kim, D.-J. Xuan, Y.-B. Kim

Proceedings Volume 6042, ICMIT 2005: Control Systems and Robotics; 604229 (2006) https://doi.org/10.1117/12.664650
Event: ICMIT 2005: Merchatronics, MEMS, and Smart Materials, 2005, Chongqing, China

Abstract

In this paper a magnetic flywheel system is studied with a magnetic bearing, which is able to support the shaft without mechanical contacts, and it is also able to control the rotational vibration. Magnetic flywheel system is composed of position sensors, a digital controller, actuating amplifiers, electromagnets and a flywheel. This work applies the fuzzy neural network (FNN) algorithm to control the vibration of a magnetic flywheel system. It proposes the design skill of an optimal controller when the system has the uncertainty, i.e. it has a difficulty in extracting the exact mathematical expressions. Two controllers are designed for the FNN in order to reduce the rotor vibration effectively. Unbalance response, which is a serious problem in rotating machineries, is improved by using a magnetic bearing with a FNN algorithm.

Citation Download Citation

J.-W. Kim, D.-J. Xuan, and Y.-B. Kim "Control of a magnetic flywheel by a fuzzy neural network algorithm", Proc. SPIE 6042, ICMIT 2005: Control Systems and Robotics, 604229 (2 May 2006); https://doi.org/10.1117/12.664650

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