• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1211-1214
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• 1993

In this paper, we present a fuzzy-number-oriented methodology to model uncertain geometric robot environment and to manipulate geometric uncertainty between robot coordinate frames. We describe any geometric primitive of robot environment as a parameter vector in parameter space. Not only ill-known values of the parameterized geometric primitive but the uncertain quantities of coordinate transformation are represented by means of fuzzy numbers restricted to appropriate membership functions. For consistent interpretation about geometric primitives between different coordinate frames, we manipulate these uncertain quantities using fuzzy arithmetic.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.545-550
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• 2004

This paper is proposed a fuzzy neural network controller based on the vector controlled induction motor drive system. The hybrid combination of fuzzy control and neural network will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed estimation and control of speed of induction motor using ANN Controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.153-156
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• 2003

This short paper suggests a control strategy using a stepwise fuzzy moving sliding surface. The moving surface is a Sugeno-type fuzzy system that has the angle of state error vector and the distance from the origin in the phase plane as inputs and a first-order linear differential equation as an output. The surface initially passes arbitrary initial states and subsequently moves towards a predetermined surface via rotating or shifting. the proposed method reduces the reaching and tracking time and improves robustness. The asymptotic stability of the fuzzy sliding surface is proved. The validity of the proposed control scheme is shown in computer simulation for a second-order nonlinear system.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1114-1115
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• 2007

In this paper, a new speed sensorless control based on a fuzzy compensator are proposed for the interior permanent magnet synchronous motor (IPMSM) drives. The conventional proportional plus integrate(PI) control are very sensitive to step change of the command speed, parameter variations and load disturbance. To cope with these problems of the PI control, the estimated speeds are compensated by using the fuzzy logic controller (FLC). In the FLC used by the speed compensator of the IPMSM, the system control parameters are adjusted by the fuzzy rule based system, which is a logical model of the human behavior for process control. The effectiveness of algorithm is confirmed by the experiments.

• Proceedings of the KIPE Conference
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• 2000.07a
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• pp.211-214
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• 2000

The conventional PI controller has been widely used in industrial application due to the simple control algorithm. But it is very difficult to find the optimal PI control gain. Therefore in this paper to obtain optimal performance fuzzy proportional-plus-integral controller for the vector control system of an induction machine is presented. The simulation model is created in MATLAB/SIMULINK. The simulation results demonstrate the good performance of this system.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.469-471
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• 1999

In this paper, the method of Space-Vector Pulse Width Modulation(SVPWM) with Fuzzy Logic Regulator(FLR) is proposed. In a conventional SVPWM, the procedures of phase transformation and choosing PWM patterns are complex. So, it should be implemented with high performance processor like Digital Signal Processor(DSP). In order to reduce a calculation burden, a proposed system adopts FLR. Using a linguistic contro strategy based on expert knowledge, FLR relieves the processor from a heavy computations. In simulations, the proposed system is validated.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.247-253
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• 2011

A sustainability of electricity supply has emerged as a critical issue for low carbon green growth in South Korea. Wind power is the fastest growing source of renewable energy. However, due to its own intermittency and volatility, the power supply generated from wind energy has variability in nature. Hence, accurate forecasting of wind speed and power plays a key role in the effective harvesting of wind energy and the integration of wind power into the current electric power grid. This paper presents a short-term wind speed prediction method based on support vector regression. Moreover, particle swarm optimization is adopted to find an optimum setting of hyper-parameters in support vector regression. An illustration is given by real-world data and the effect of model regularization by particle swarm optimization is discussed as well.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.100-104
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• 2003

The SVDD(support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the kernel feature space in order to distinguish a set of normal data from all other possible abnormal objects. The major concern of this paper is to consider the problem of modifying the SVDD into the direction of utilizing ellipsoids instead of balls in order to enable better classification performance. After a brief review about the original SVDD method, this paper establishes a new method utilizing ellipsoids in feature space, and presents a solution in the form of SDP(semi-definite programming) which is an optimization problem based on linear matrix inequalities.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.2
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• pp.141-146
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• 2009

Support vector machine is a classification algorithm based on statistical learning theory. It has shown many results with good performances in the data mining fields. But there are some problems in the algorithm. One of the problems is its heavy computing cost. So we have been difficult to use the support vector machine in the dynamic and online systems. To overcome this problem we propose to use stratified sampling of statistical sampling theory. The usage of stratified sampling supports to reduce the size of training data. In our paper, though the size of data is small, the performance accuracy is maintained. We verify our improved performance by experimental results using data sets from UCI machine learning repository.

• Proceedings of the KIPE Conference
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• 2002.11a
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• pp.21-24
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• 2002

The vector control system of an induction motor is the high performance drive system to achieve the instantaneous torque control. The vector control system is greatly divided into the direct control, and the indirect control that the most widely is used, The indirect vector control needs the rotor time constant, which changes widely according to the temperature, frequency, and current amplitude. The incorrect time constant leads to the saturation of magnetic flux or under-excitation phenomena. As a result, that deteriorate the control performance. Therefore, in this paper, the effect of time constant variation is investigated and its on-line tuning algorithm is proposed. The time constant using the torque angles was calculated and that of the validity of algorithm proposed was proved through the computer simulation and the experiment.