• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.732-735
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• 1999

A GA-based optimization technique is adopted in the paper to obtain optimal future control inputs for predictive control systems. For reliable future predictions of a process, we identify the underlying process with an NNARX model structure and investigate to reduce the volume of neural network based on the Lipschitz index and a criterion. Since most industrial processes are subject to their constraints, we deal with the input-output constraints by modifying some genetic operators and/or using a penalty strategy in the GAPC. Some computer simulations are given to show the effectiveness of the GAPC method compared with the adaptive GPC algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.129.4-129
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• 2001

The looper control of hot strip finishing mill is one of the most important control item in hot strip rolling mill process. Loopers are placed between finishing mill stands and control the mass flow of the two stands. Another important action of the looper is to control the strip tension which influences on the width of the strip. So it is very important to control both the looper angle and the strip tension simultaneously but the looper angle and the strip tension are strongly interacted by each other. There are many control schemes such as conventional, non-interactive, LQ(Linear Quadratic), Hinf and ILQ(Inverse Linear Quadratic), Adaptive(gain scheduling) control in the looper control system. In this paper, we present the modeling for the looper of a hot strip finishing mill to control the tension of the strip and suggest another control method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6C
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• pp.343-348
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• 2011

This paper introduces a design for real time wireless headset using ANC (active noise control) system based on NFxLMS adaptive filter algorithm. The training time of the proposed system is significantly reduced by using the RMS delay spread of a channel as an error correction parameter, and convergence rate of the FxLMS filter has been improved with updating the coefficients of the NFxLMS filter, which we have got during the training process. Our system has shorter training time and better convergence rate at the same noise reduction level than the conventional system under real noisy environment.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.3
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• pp.62-68
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• 2006

In this paper, an adaptive input-output linearization and zero dynamics control of three phase AC/DC converter are proposed. For achieving output do voltage regulation with unity power factor, the q-axis current of the rotating d-q frame is regulated to zero and the output do voltage is controlled to track a given reference voltage $V_r$. The proposed scheme is robust to the parametric uncertainty md load current of the converter due to the adaptation process. The simulation results are presented to illustrate the performance and feasibility of the proposed control scheme.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.903-905
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• 2015

The need for digital devices is increasing in the digital age. In general, noise in images occurs during the process of compression, recognition and processing due to many reasons. Some of the filters used to remove salt and pepper noise include SMF, CWMF and AMF. In areas where the noise density is high, the removal of noise is undermined. This paper suggests an adjusted median filter algorithm that preserves the non-noise pixels while transforming the noise pixels to more effectively remove salt and pepper noise.

• ICROS
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• v.2 no.1
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• pp.59-71
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• 1996

In the last decade, the model predictive control methods have enjoyed many industrial applications with successful results. Although the general predictive control methods for nonlinear chemical processes are not yet formulated, the promising features of the model predictive control methods attract attentions of many researchers who are involved with difficult but important nonlinear process control problems. Recently, the class of bilinear model has been introduced as an useful tool for examining many nonlinear phenomena. Since their structural properties are similar to those of linear models, it is not difficult to develop a robust adaptive model predictive control method based on bilinear model. We expect that the model predictive control method based on bilinear model will expand its region in the world of nonlinear systems.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.2
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• pp.197-203
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• 1997

In this paper, we present a GP-PID(Generalized Predictive PID) controller which has the same structure as a generalized predictive control with steady-state weighting. The proposed controller can perform better than the conventional PID controller because it includes intrinsic delay-time compensator. The PID tuning parameters and delay-time compensator are calculated by equating the two degree of freedom PID to a linear form of GPC. The proposed controller is combined with a supervisor for safe start and self-tuning. GP-PID controller has been tested for various numerical models and an experimental stirred tank heater. As a result, it was observed that the proposed controller shows a satisfactory performance for variable delay as well as stochastic disturbance.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.2
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• pp.130-135
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• 2000

A two parameter model of a single fictitious weak acid with unknown dissociation constant has been successfully applied to design a neutralization system for many multi-component acid streams. But there are some processes for which above two parameter model is not satisfactory due to poor approxmation of the nonlinearity of pH process. Here, for etter control of wide class of multi-component acid streams, a three parameter model of a strong acid and a weak acid with unknown dissociation constant is proposed. The model approximates effectively three types of largest gain variation nonlinearities. Based on this model a nonlinear pH control system is designed. Parameters can eeasily estimated since their combinations appear linearly in the model equations and nonlinear adaptive control system may also be constructed just as with the two parameter model.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.6
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• pp.547-554
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• 2007

This thesis presents a kind of active noise control(ANC) algorithm for reducing noise due to engine inside a car. The proposed control algorithm is, by using WP(Wavelet Packet), a one improving the instability due to delay of noise transmission and the lack of response ability for the rapid change of noise, which are defects of the existing FXLMS(Filtered-X Least Mean Square) algorithm. The chief character of this system is a thing that faster operation than the FXLMS is implemented by inserting WP in the secondary path. In other words, WP implements parallel operation. Then, the weights of filter in the adaptive algorithm will be updated faster. In addition, because WP have so excellent a resolution, they can process very minute noise. The efficiency of this control algorithm will be demonstrated in the matlab simulation and in the actual experiments by using a Labview program and a car.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.1
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• pp.51-65
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• 2002

This paper presents a design method of the neural network-based controller using an indirect adaptive control method to deal with an intelligent control for chaotic nonlinear systems. The proposed control method includes the identification and control Process for chaotic nonlinear systems. The identification process for chaotic nonlinear systems is an off-line process which utilizes the serial-parallel structure of multilayer neural networks and simple state space neural networks. The control process is an on-line process which uses the trained neural networks as the system model. An error back-propagation method was used for training of identification and control for chaotic nonlinear systems. The performance of the proposed neural network controller was evaluated by application to the Duffing equation and the Lorenz equation, and the proposed controller was compared with other neural network-based controllers by computer simulations.