• The Transactions of the Korean Institute of Power Electronics
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• v.10 no.6
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• pp.534-542
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• 2005

This paper proposes the fuzzy logic based vector control method for a Surface Mounted Permanent Magnet Synchronous Motor(SMPMSM) used in the elevators. The gain of a conventional PI speed controller in the elevator drive system can not be usually set high due to mechanical resonances, therefore its performance becomes deteriorated. There have been many methods to solve above problems such as an acceleration feedback in the speed controller. However, the above methods have defects that parameter information is demanded. In this paper', a Fuzzy controller(FC) is adopted in the elevator drive system. The performance of a fuzzy controller is compared with a PI controller in the no load and load conditions by simulation and experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.445-445
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• 2000

This paper proposes the MIMO indirect adaptive fuzzy controller to control the two-link manipulators. The input-output linearization technique, equivalent control input plus integral term, augmented error model and recursive least square adaptive law are used fer the controller. The linear type of fuzzifier-defuzzifier fuzzy logic system used for nonlinear function makes easy to farm the error model and able to follow the adaptive system approach. Such that control approach, the control system is not required joint speed and accerelation measurement and easy to implement and tune. The simulation results showed that the proposed controller has good control performance, stability, very small tracking error, decoupling, fast convergence, robust to parameter variation and load.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.353-356
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• 1997

Vehicle steering system determines the direction of a vehicle. A manual steering system consists of mechanical connections between the steering wheel and tires. Recent power steering system adds an actuator to help a driver to steer easily at low speed. However, at front collision, the driver can be injured by steering shaft and the power steering pump decreases the engine power. To solve these problems, electronic power steering system which connects the steering wheel and tires with electronic connection is proposed, that has advantages such as decrease of engine load and increase of driver safety reactive. Since the ratio between driver's steering torque and steering torque of tires can be controlled freely, the torque which is delivered from the road to the driver through tires and steering wheel can be reshaped to make the driver feel comfortable. In this paper, the ratio of delivering steering torque and the magnitude of force to be delivered from road to driver has been controlled using fuzzy controller, and it's effectiveness has been shown through simulation results.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1113-1114
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• 2011

This paper is proposed design of high performance controller of SynRM drive using FNN and NNC. Also, This paper is proposed of designing fuzzy neural network controller(FNNC) which adopts the fuzzy logic to the artificial neural network(ANN). FNNC combines the capability of fuzzy reasoning in handling uncertain information and the capability of neural network in learning from processes. This controller is controlled speed using FNNC and model reference adaptive fuzzy control(MFC), and estimation of speed using ANN. The performance of proposed controller was demonstrated through response results. The results confirm that the proposed controller is high performance and robust under the variation of load torque and parameters.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.12
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• pp.2352-2360
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• 2007

Due to the friction characteristics of cylinders and the rail of a passenger car, in the elevator actuated with hydraulic systems, there exist dead zones, which can not be controlled by a PID controller. In this paper, the friction characteristics of a cylinder is examined, which may cause the abrupt increase of the acceleration in the zero-costing speed region. To overcome the drawbacks of a PID controlled hydraulic elevator system, a zooming fuzzy logic controller is designed and finally an improved hybrid fuzzy controller is proposed. The effectiveness of the proposed control scheme are shown by simulation and experimental results, which the proposed fuzzy hybrid control method yields good control performance not only in the zero-crossing speed region but also in the overall control region including steady-state region.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.107-111
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• 1998

This paper presents a now approach to the design of intelligent contorl system for track vehicle system using fuzzy logic based on neural network. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. Moreover, We develop a Windows 95 version dynamic simulator which can simulate a track vehicle model in 3D graphics space. It is proposed a learning controller consisting of two neural network-fuzzy based of independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The dynamic simulator for track vehicle is developed by Microsoft Visual C++. Graphic libraries, OpenGL, by Silicon Graphics, Inc. were utilized for 3D Graphics. The performance of the proposed controller is illustrated by simulation for trajectory tracking of track vehicle speed.

• Proceedings of the KIEE Conference
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• 1995.07a
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• pp.251-254
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• 1995

The Fuzzy-Neural Controller is constructed to resolve some dificulties taking place in decision of membership functions, input and output gains and an inferenced method for desinging fuzzy logic controller. In addition Neural network emulator is used to emulate induction motor forward dynamics and to get error signal at fuzzy-neural controller output layer. Error signal is backpropagated through neural network emulator. A back propagation algorithm is used to train fuzzy-neural controller and emulator. The experimental results show that this control system can provide good dynamical responses.

• Proceedings of the KIEE Conference
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• summer
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• pp.1385-1387
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• 2004

This paper presents a modelling methodology using fuzzy logic and train performance simulation for determining an economical running pattern for a high speed train which minimizes energy consumption under an given trip margin. The economical running pattern is defined with an economical maximum speed in traction phase, a speed at the end of coasting. As a case study, the simulation is carried out for an economical run of korea high speed train prototype, and the results of fuzzy model described.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.443-447
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• 2001

In this paper, a robust sensorless vector control system for induction motors with a speed estimator and an uncertainty observer is presented. At first, the proposed speed estimator is based on the MRAS(Mode Reference Adaptive System) scheme and constructed with a simple fuzzy logic(FL) approach. The structure of the proposed FL estimator is very simple. The input of the FL is the rotor flux error difference between reference and adjustable model, and the output is the estimated incremental rotor speed Secondly, the unmodeled uncertainties such as parametric uncertainties and external load disturbances are modeled by a radial basis function network(RBFN). In the overal speed control system, the control inputs are composed with a norminal control input and a compensated control input, which are from RBFN observer output and the modeling error of the RBFN, repectively. The compensated control input is derived from Lyapunov unction approach. The simulation results are presented to show the validity of the proposed system.

• Journal of Welding and Joining
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• v.16 no.5
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• pp.119-133
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• 1998

To implement full automation in pipe welding, it si most important to develop special sensors and their related systems which act like human operator when detecting irregular groove conditions. In this study, an automatic pipe Gas Metal Arc Welding (GMAW) system was proposed to full control pipe welding procedure with intelligent sensor systems. A five-axes manipulator was proposed for welding torch to automatically access to exact welding position when pipe size and welding angle were given. Pool status and torch position were measured by using a weld-pool image monitoring and processing technique in root-pass welding for weld seam tracking and weld pool control. To overcome the intensive arc light, pool image was captured at the instance of short circuit of welding power loop. Captured image was processed to determine weld pool shape. For weld seam tracking, the relative distance of a torch position from the pool center was calculated in the extracted pool shape to move torch just onto the groove center. To control penetration of root pas, gap was calculated in the extracted pool image, and then weld conditions were controlled for obtaining appropriate penetration. welding speed was determined with a fuzzy logic, and welding current and voltage were determined from a data base to correspond to the gap. For automatic fill-pass welding, the function of human operator of real time weld seam control can be substituted by a sensor system. In this study, an arc sensor system was proposed based on a fuzzy control logic. Using the proposed automatic system, root-pass welding of pipe which had gap variation was assured to be appropriately controlled in welding conditions and in torch position by showing sound welding result and good seam tracking capability. Fill-pass welding by the proposed system also showed very successful result by tracking along the offset welding line without any control of human operator.