• 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.

• International Journal of Aeronautical and Space Sciences
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• v.6 no.1
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• pp.27-43
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• 2005

An INS(Inertial Navigation System) is composed of a navigation computer and an IMU(Inertial Measurement Unit), and can be applied to estimate a vehicle's state. But the inertial sensors assembled in the IMU are too complicated and expensive to use for the general application purpose. In this study, a new concept of inertial sensor system using magnetic levitation is proposed. The proposed system is expected to replace one single-axis rate or position gyroscope, and one single-axis accelerometer concurrently with a relatively simple structure. A simulation of the proposed system is given to describe the capability of this new concept.

• Proceedings of the Korean Magnestics Society Conference
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• 1991.11a
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• pp.12-16
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• 1991

The advantage of the controlled permanent magnet system for magnetic levitation is the considerable improvement in levitation force to weight ratio and the reduced rating of the on-board power supply. A combined support and guidance technique with selfstable guide behaviour reduce the expense of control and power electronics. An important feature of this system is the simplified mechanical construction of track and the vehicle does not embrace the guide way. Therefore surface mounted as well as elevated tracks are possible. The magnet was designed to provide a lift force of 750kg for a 3 ton prototype vehicle with 4 magnets by KERI and built by Dae-Bo Magnetic Co., LTD. The paper also describes the assembling technology for permanent magnets, the experimental setup and test results for lift force, guidance force and flux distribution.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.8
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• pp.1308-1316
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• 2001

A hybrid type master system is proposed to take the advantage of the link mechanism and magnetic levitation mechanism without using a force sensor. Two different types of electromagnetic actuators, moving coil type and moving magnet types are used to drive the master system which is capable of 4-DOF actuation. It is designed that the rotation motions about x-y axis are decoupled and the whole system is represented by simple dynamic equations. The force reflection is achieved by using the simple relation between the force and applied current and position. The simulation and experimental results are presented to show its performance.

• 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 KIEE Conference
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• 2001.07d
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• pp.2120-2122
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• 2001

This paper describes a PID controller for an attraction type magnetic levitation system considering $H^{\infty}$ performance. PID controllers are a kind of fixed structure reduced order controllers. Using change of variables a reduced order control problem can be recast into a static output feedback control problem and can be solved efficiently by LMI(Linear Matrix Inequality) method. In this paper, cone complementarity method is adopted and the simulation shows good results.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.2
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• pp.226-235
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• 1992

This paper is concerned with the robust control of LQ state feedback regulators with poles in a specified region in the presence of system uncertainty. The robust stability results for the constant and nonlinear time varying perturbations are derived in terms of bounds of the perturbed system matrices and the weighting matrices in the performance index of LQ problem. The theoretical results are applied to the gap control problem of an attractive-type-magnetic levitation system and the effectiveness is proved by the implementation of digital control using 16 bits microcomputer.

• 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.

• 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 KIEE Conference
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• 2005.07b
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• pp.1327-1329
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• 2005

This paper presents a gam optimization method using genetic algorithm(GA) for a magnetic levitation(Maglev) controller. GA uses the integral of square error(ISE) as performance index. The plant dynamics are described and modelled by mathematical equations. Also, the system apparatus for the Maglev system are described. Using the derived model, to optimize the feedback gains of conventional state feedback controller(SFC), GA is simulated with SIMULINK model. finally, using the optimized feedback gains, SFC is applied to the Maglev system. From the results, we can see that GA can give a solution for the better control performance for the Maglev system.