• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.647-648
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• 2006

In this paper, magnetic levitation characteristics of hybrid magnets are experimentally shown under the newly proposed airgap condition. The digital PID controller is utilized to control the airgap of the magnetic levitation system.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.37.6-37
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• 2001

In this paper, a robust levitation controller for an attractive MAGLVE system is designed. The design of an H$\infty$ controller based on LMI method is proposed for the control of a simple magnetic levitation system. Attractive MAGLEV system is highly nonlinear and open-loop unstable, and has a very restricted equilibrium region, Also, this system has to tolerate various disturbances caused by propulsion. Thus a robust feedback controller is needed to control the system efficiently. We first formulate a mathematical model for the single magnet levitation system. Then we set up an H$\infty$ control problem as a mixed sensitivity problem where the augmented plant is constructed with frequency weighting function ...

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.339-344
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• 1993

An H$_{\infty}$ control system design for a magnetic levitation system is presented. In the control system design, we consider the influence of both disturbances and uncertainties in the model. The main disturbances stem from the position sensors.The uncertainties are divided into electromagnetic and mechanical ones: the former are due to the gain change in the current amplifier, the influence of leakage flux and modelling error in the magnetic circuit and the latter are due to the changes of the mass and the moments of inertia of the vehicle. Therefore, the designed controller is indispensable to guarantee the robustness of this system for both stability and performance. The controller design is based on the standard H$_{\infty}$ optimal control problem. As the novel features in this paper :(1) there are two poles on j.omega.-axis in the control model;(2) an integrator is included in the controller so that equivalently there are three poles on j.omega.-axis in the model. Finally, several experiments and simulations are carried out to verify the high performance and robustness of the designed control system.m.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.499-501
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• 1998

In this paper, we present a self-compensating PID controller which consists of a conventional PID controller that controls the linear components and a neural controller that controls the higher order and nonlinear components. This controller is based on the Harris's concept where he explained that the adaptive controller consists of the PID control term and the disturbance compensating term. The resulting controller's architecture is also found to be very similar to that of Wang's controller. This controller adds a self-tuning ability to the existing PID controller without replacing it by compensating the control errors through the neuro-controller. When applied to an actual magnetic levitation system which is known to be very nonlinear, it has also produced an excellent results.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.59-70
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• 1995

Output performance improvement using fuzzy logic to the conventional control scheme for a magnetic levitation system is presented in this paper, Adverse characteristics of nonlinearity, unstability, system parameter variation, etc, in the levitation system are partially overcome by the general fuzzy control action. Using a PD type compensator, a coarse framework of output performance is provided to the levitation system. Then a fine regulation to the output performance requirement is obtained by the natural description of the control action in the form of fuzzy logic controller. This control action soothes the adverse characteristics of the levitation system. In this way a better output performance can be obtained in a real time experiment.

• Proceedings of the KIEE Conference
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• 2003.07b
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• pp.864-866
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• 2003

In this paper, the improvement of control performance for a magnetic levitation system is presented. The model of a single-magnet system with rail is derived. As a conventional controller, PID controller is designed and implemented. This controller has a narrow stability margin due to low-order controller structure so that it cause unstability for a flexible rail. So to improve the control performance and stability margin, a robust controller as a new controller is designed and implemented using a VME-based digital controller. As a result, the controller performance of the new designed controller is better than that of the conventional controller.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.10a
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• pp.339-340
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• 2006

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.86-86
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• 2000

This paper describes a neural network based fuzzy type PID control scheme. The PID controller is being widely used in industrial applications. however, it is difficult to determine the appropriate PID gains for (he nonlinear system control. In this paper, we re-analyzed the fuzzy controller as conventional PID controller structure, and proposed a neural network based fuzzy type PID controller whose scaling factors were adjusted automatically. The value of initial scaling factors of the proposed controller were determined on the basis of the conventional PID controller parameters tuning methods and then they were adjusted by using neural network control techniques. Proposed controller was simple in structure and computational burden was small so that on-line adaptation was easy to apply to. The result of practical experiment on the magnetic levitation system, which is known to be hard nonlinear, showed the proposed controller's excellent performance.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.1
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• pp.22-30
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• 2001

This paper describes a neural network based fuzzy PID control scheme. The PID controller is being widely used in industrial applications. However, it is difficult to determine the appropriated PID gains in nonlinear systems and systems with long time delay and so on. In this paper, we re-analyzed the fuzzy controller as conventional PID controller structure, and proposed a neural network based self tuning fuzzy PID controller of which output gains were adjusted automatically. The tuning parameters of the proposed controller were determined on the basis of the conventional PID controller parameters tuning methods. Then they were adjusted by using proposed neural network learning algorithm. Proposed controller was simple in structure and computational burden was small so that on-line adaptation was easy to apply to. The experiment on the magnetic levitation system, which is known to be heavily nonlinear, showed the proposed controller's excellent performance.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.4
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• pp.395-401
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• 2014

Magnetic levitation systems using the attraction force of electromagnets have many constraints according to the variation of air gap and the nonlinearity of electromagnetic force and inductances. As a result of these constraints, the nonlinear control of a magnetic levitation system has been improved by the latest advanced processors and accurate measurement system which can overcome problems such as many constraints and nonlinearity. This paper concentrates on the modeling of a nonlinear magnetic levitation system and an application of an exponential reaching law based sliding mode controller using the exponential reaching law which is one of the most robust controllers against external unexpected disturbances or parameter fluctuations. Controllability of a magnetic levitation system using the sliding mode control algorithm and robustness against parameter fluctuations have been verified through the experimental results.