• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.5
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• pp.812-818
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• 2017

In this paper, we design a reduced-order disturbance observer (DOB) controller for an EMS (Electro-Magnetic Suspension) system with mass uncertainty. Compared with conventional DOB controller, the proposed reduced-order DOB controller can be implemented in a simpler way, since it uses reduced order nominal model and Q-filter. It is shown that the nominal model for the proposed DOB controller should be carefully chosen in order to achieve the robust stability in the present of mass uncertainty. Computer simulation results to validate the effectiveness of the proposed DOB controller are included.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1143-1145
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• 1996

In this paper, we consider a nonlinear control problem for an Electro-Magnetic Suspension(EMS) system with flexible rail. In controller design based on feedback linearization, we apply the feedback linearization technique to the part of the system which provides nonlinearities to the plant. The experimental results demonstrate that the feedback linearization controller shows good performance.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.268-270
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• 1993

The implementation of Nonlinear Feedback Linearization control for Electro-Magnetic Suspension system is presented. The controller using TMS320C31 DSP chip was proposed and the experiments were performed Control law for EMS system using feedback linearization is derived and implemented in the DSP. Some tests were constructed far experimental comparison between feedback linearization and classical state feedback The experimental results demonstrate that the feedback linearization controller shows bettor performance than that of the classical state feedback controller and it is robust with respect to disturbance and parameter variation, though some steady-state errors appear.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.752_753
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• 2009

This paper deals with controller design and dynamic simulation of hybrid magnetic bearing. The flux density at air-gap is obtained from system modeling which considers permanent magnet and electro magnet. The vertical force is derived yb that flux density using maxwell's stress tensor.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.269-275
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• 2013

In this paper, a new modeling method of a magnetic levitation(Maglev) system using extreme learning machine(ELM) is proposed. The linearized methods using Taylor Series expansion has been used for modeling of a Maglev system. However, the numerical method has some drawbacks when dealing with the components with high nonlinearity of a Maglev system. To overcome this problem, we propose a new modeling method of the Maglev system with electro magnetic suspension, which is based on ELM with fast learning time than conventional neural networks. In the proposed method, the initial input weights and hidden biases of the method are usually randomly chosen, and the output weights are analytically determined by using Moore-Penrose generalized inverse. matrix Experimental results show that the proposed method can achieve better performance for modeling of Maglev system than the previous numerical method.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.2
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• pp.20-30
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• 2001

In the paper, we propose a predictive control scheme using multiple neural network-based prediction models. To construct the multiple models, we select several specific values of a parameter whose variation affects serious control performance in the plant. Among the multiple prediction models, we choose one that shows the best predictions for future outputs of the plant by a switching technique. Based on a nonlinear programming method, we calculate the current process input in the nonlinear predictive control system with multiple prediction models. The proposed control method is shown to be very effective when a parameter of the plant changes or the time delay, if it exists, varies. It is also shown that the proposed method is successfully applied for the control of suspension in a electro-magnetic levitation system.