• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.5
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• pp.72-78
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• 2006

The paper proposes reference model error feedback control scheme for motion control system with hard non-linear components as like saturation and dead-zone in plant input part. Additionally, the plant has the system uncertainty effected by plant model parameter deviation and disturbance. The control algorithm uses the reference model to apply additional feedback loop with the error between reference model output and actual output effected by disturbance and non-linear components. And the stability evaluation based on Popov stability and controller design method are formulated to be performed. The effectiveness of the proposed scheme is examined by simulations. The results are proven by reasonable performances following reference model responses with good disturbance rejection performance without over-tuning of controller.

• Journal of the Korean Society of Industry Convergence
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• v.3 no.1
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• pp.3-8
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• 2000

This paper describes a development of self tuning scheme for PI and PO type fuzzy controllers. The output scaling factor(SF) is adjusted on-line by fuzzy rules according to the current trend of the controlled process. The rule-base for tuning the output SF is defined on error and change of error for the controlled variable using the most natural and unbiased membership functions. Simulation results demonstrate the better control performance can be achieved in comparison with Ziegler-Nichols(Z-N) PID controllers.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.1
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• pp.58-67
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• 2020

A big data processing method to predict solar power generation using systems engineering approach is developed in this work. For developing analytical method, linear model (LM), support vector machine (SVN), and artificial neural network (ANN) technique are chosen. As evaluation indices, the cross-correlation and the mean square root of prediction error (RMSEP) are used. From multi-variable comparison test, it was found that ANN methodology provides the highest correlation and the lowest RMSEP.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.18 no.4
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• pp.21-29
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• 2010

The main purpose of this study is to obtain linear models for the design of automatic flight controller in order to operate the Light Sport Aircraft as unmanned air vehicle. Flight test equipments installed on the aircraft to acquire flight test data are described and maneuvers for practical speed calibration are introduced. Parameters for the linear models of lateral and longitudinal motion are estimated by the Output error method as well as trim data analysis using the flight test data. Simulated data using the estimated parameters is shown to agree well with the measurement data. Estimated parameters obtained for several flight conditions can be used to improve the aerodynamic database of the simulation program.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.298-306
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• 1993

A portable torque and power measurement system for small farm equipment based on instrumented pulley was developed. The prototype pulley was machined from mild steel, with spokes serving as strain beams. Strain gages mounted to the spokes sense the bending strain due to the torque and convert this into millivolt output. Calibration results showed the torque-millivolt relationship was linear, while hysterisis and error were less than 1% fs. For power measurements, an additional tachmeter with dcvoltage output is necessary. With the tachometer , error in power measurement was +-1.03W or 0.2% fs. Field tests showed that for ease of installation, no machine alteration needed and safety, this system had advantage over other methods for small farm equipment.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.621-623
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• 2000

In this paper, the effective design method of the predictive controller using fuzzy neural networks(FNNs) is presented for the Intelligent control of chaotic nonlinear systems. In our design method of controller, predictor parameters are tuned by the error value between the actual output of a chaotic nonlinear system and that of a fuzzy neural network model. And the parameters of predictive controller using fuzzy neural network are tuned by the gradient descent method which uses control error value between the actual output of a chaotic nonlinear system and the reference signal. In order to evaluate the performance of our controller, it is applied to the Duffing system which are the representative continuous-time chaotic nonlinear systems and the Henon system which are representative discrete-time chaotic nonlinear systems.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.7
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• pp.253-257
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• 2013

This paper proposes the robust observer design for nonlinear systems with delayed output and external disturbance. It is shown that by considering a nonlinear term of error dynamics as an additional state variable, the nonlinear error dynamics with time delay can be transformed into the linear one with time delay. Sufficient conditions for existence of a robust observer are characterized by linear matrix inequalities. Finally, an illustrative example is given in order to show the effectiveness of our design method.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.22-25
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• 1999

A brushless DC motor has the high quality of torque output and silence, has been more widely used in industrial area. As the driver and controller of BLDC motor have been more complicated and precise, simulation method has been much used in motor design. And the output characteristics of BLDC motor is determined by the waveform of BACK-KMF in instinct. But because the conventional model of BLDC motor is obtained by approximation of real nonlinear waveform to ideal trapezoidal waveform, the error is occurred in simulation result. Thus in this paper, for the correction of this error in simulation, the model of real nonlinear waveform considered is proposed, and the simulation result is obtained in case of three-phase, four-poles Y-connected, surface mounted permanent magnet BLDC motor.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07b
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• pp.1234-1238
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• 2004

A Controlled neural networks are proposed in order to measure nonlinear environments in adaptive and in realtime. The structure of it is similar to recurrent neural networks: a delayed output as the input and a delayed error between tile output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. This new neural networks is Error Estimated Neural Networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models. To show the performance of this one, we have various experiments. And this controller call prove effectively to be control in the environments of various systems.

• Proceedings of the KIEE Conference
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• 2003.07e
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• pp.49-51
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• 2003

This paper presents a speed control system of induction motor using neural network. The speed control of induction motor was designed to NNC(Neural Network Controller) and NNE(Neural Network Estimator) used backpropagation, the NNE was constituted to be get an error value of output of an induction motor and conspire an input/output. NNC is controled to be made the error of reference speed and actual speed decrease, and in order to determine the weighting of NNC can be back propagated through the NNE, and it is adapted to the outside circumstances and system characters with learning ability.