This paper describes the neural network control method for the identification and control of chaotic nonlinear dynamical systems effectively. In our control method, the controlled system is modeled by an unknown NARMA model, and a feedforward neural network is used for identifying the chaotic system. The control signals are directly obtained by minimizing the difference between a setpoint and the output of the neural network model. Since learning algorithm guarantees that the output of the neural network model approaches that of the actual system, it is shown that the control signals obtained can also make the real system output close to the setpoint.

In this paper, we propose a scaling factor tuning method to improve the performance of fuzzy controller. Tuning rules and reasoning are utilized on-line to determine the scaling factors based on absolute value of the error and its difference. A adaptive evolutionary computation (AEC) is used to search for the optimal tuning rules that will maximize the fitness function. Finally, the proposed fuzzy controller is applied to the angular stabilization of an inverted pendulum.

In order to improve the tracking performance of $2{\times}2$ multivariable control systems, a fuzzy control algorithm with feedforward compensator is represented. The method consists in two steps. First, neglecting interconnections. one designs a fuzzy controller to each individual loop. In the second stage, low-order transfer functions of outputs to reference inputs are estimated. We propose a design method of the feed forward compensator based on the transfer functions. An illustrative example are shown.

A passive approach for enhancing fault detection and isolation performance of multiple observer based fault detection isolation schemes(FDIS) is proposed. The FDIS has a hierarchical framework to perform detection and isolation of faults of interest, and diagnosis of process faults. The decision unit comprises of a rule base and fuzzy inference engine and removes some difficulties of conventional decision unit which includes crisp logic and threshold values. Emphasis is placed on the design and evaluation methods of the diagnostic rule base. The suggested scheme is applied for the FDIS design for a DC motor driven centrifugal pump system.

One significant error in XY table is due to friction and disturbance. However, the characteristics of this friction is not easy to predict and analyze because of its nonlinearity. Therefore, it is difficult for conventional controller to compensate it effectively. In order to solve this problem, this paper presents a position controller based on fuzzy logic controller(FLC) that is suitable for system with unknown and unmodelled dynamics. The performance of the proposed controller are demonstrated by simulation results.

In a large diesel engine actuator position servo system, it is impossible to isolate an actuator fault from a load torque with conventional fault detection isolation (FDI) schemes because they are propagated through a channel. This paper deals with a parity equation based residual generation to isolate them in the system. The actuator fault is modelled by a multiplicative type fault that can be characterized as discrepancies between the nominal and actual plant parameters, whereas the load torque is modelled by an additive disturbance. The transformation implemented in the residual generator should be determined on-line to achieve the isolation. Simulation studies show the practical applicability of the FDI scheme.

All Fault Detection and Diagnosis(FDD) methods utilize classification techniques. The objective of this study was to demonstrate the application of classification techniques to the problem of diagnosing faults in data generated by a variable-air-volume(VAV) air-handling unit(AHU) simulation model and to describe the characteristics of the techniques considered. Artificial neural network classifier and fuzzy clustering classifier were considered for fault diagnostics.

In this paper, a novel Programmable Signal Conditioner(PSC) for Thermo Couple(T/C) without variable-resistance is proposed. It is fabricated by using a fully digitalized error-correction and calibration algorithm. In signal processing of T/C, since the output voltage of T/C is nonlinear and its level is very low, the circuitry become very complicated to reduce the converting error and identify the true thermal voltage signal. The newly proposed PSC has compensation and calibration algorithm not using variable resistor. Moreover structure can be very simple and it has highly precise output characteristics.

In this paper we describe a real time signal processing technique for the code correletion short range measurement sensor. Code correlation values are acquired via simple RC charging circuits within several tens of microsecond. Because the range measuring process is very fast and simple, the proposed technique is applicable to common near range targets as well as fast moving targets in real time. Some experimental results show the validity and usefulness of the proposed method.

This paper present a generalized observer and its existence condition. The observer can be estimated the states of system even if an external noise is added to the system's output. From a numerical example, we show that the proposed observer does not affected by a step or a sinusoidal noise.

본 논문은 비선형 시스템에서 시스템의 출력이 원하는 궤적을 따라가도록 하기 위한 반복제어 이론을 소개한다. 본 논문에서는 전체 시스템의 안정화를 위해서 추적오차의 부호에 따라 부호가 결정되는 부호전환 제어(switching control) 입력을 사용하고 있다. 그러나 본 논문에서 사용하는 제어 입력은 크기가 추적오차의 크기에 비례하게 되어 있어서 정상상태에서 발생할 수 있는 고속의 부호전환 문제(chattering problem)를 크게 완화시켜서 정상상태에서의 성능을 개선시키고 있다. 또한, 오차가 어떤 범위를 벗어나 있으면 그 범위 안으로 지수적으로 수렴하여 그 안에 계속 머물도록 하는 제어 기법도 소개하였다.

In this paper, we propose a centralized $H^{\infty}$ state estimator for the multi state estimation problem using the result suboptimal $H^{\infty}$ filter is a special form of Ka filter whose state equations are defined in md inner product spaces. Con- ventional decentr filters are based on Kalman filter assumes precesses and measurements noises are w Gaussian noise. Therefore, Kalman based decent filter design hasn't robust performance in situation. Simulation results show that decent $H^{\infty}$ filter has robust perfotmance in worst case sensor fault situation.

In this paper, new sliding mode surfaces are proposed by defining novel virtual states. These sliding surfaces have nominal dynamics of an original system and makes it possible that the Sliding Mode Control(SMC) technique is used with the various types of controllers. Its design is based on the augmented system whose dynamics have m-th higher order than those of the original system where m is the number of inputs. The reaching Phase is removed by setting the initial virtual states which makes the initial switching functions equal to zero.

In this paper, an alternate method for state-covariance assignment for SISO(single input single output) linear systems is proposed. This method is based on the inverse solution of the Lyapunov matrix equation and the resulting formulas are similar in structure to the formulas for pole placement. Further, the set of all assignable covariance matrices to a SISO linear system is also characterized.

In this paper, a novel sliding surface is proposed by introducing a virtual state. This sliding surface has nominal dynamics of an original system and makes it possible that the Sliding Mode Control(SMC) technique is used with the various types of controllers. Its design is based on the augmented system whose dynamics have one higher order than that of the original system. The reaching phase is removed by using an initial virtual state which makes the initial switching function equal to zero.

In this paper, we consider the problem of the robust stability of uncertain linear systems with multiple time-varying delays. The considered uncertainties are both the unstructured uncertainty which is only known its norm bound and the structured uncertainty satisfying the matching conditions, respectively. We present conditions that guarantee the robust stability of systems based on Lyapunov stability theorem and $H_{\infty}$ theory in the time domain. Finally, we show the usefulness of our results by numerical examples.

Asymmetric cylinders are usually used as an actuator of active suspensions. The conventional optimal controller design does not include actuator dynamics as a state. and force controller is needed to track the desired force. But the actuator is not ideal, so performance of an active suspension system is degraded. In this paper, we take account nonlinear actuator dynamics and obtain a linear model using a feedback linearization technique then apply optimal control method. For real time application, gain-scheduling method is used. Effectiveness of proposed method is demonstrated by numerical simulation of 1/4 car model.

This paper designs a noise controller for the small cavity using Coefficient Diagram Method(CDM). In the small cavity system, there exist nonlinear characteristics such as uncertain-time delay and parameter variation. In the controller design of nonlinear system with uncertainty need to the higher order controller or complexity computation. The coefficient diagram is convenient implementation of the control system design method, that is utilized as a vehicle to collectively express the important features of the system and an improved version Kessler's standard form and the Lipatov stability condition of a constitutes the theoretical basis. Simultaneously, it is provided a desired specification, such as the robustness, the stability, faster response, and lower order controller. A simulation of the system with the proposed controller shows sufficient noise cancelation in small cavity.

The drum level control is initiated by 1-element manual control, and then the control mode is changed to 1-element automatic control mode. Finally, the drum level control is changed to 3-element automatic control mode by the logic based on pre-defined threshold of main steam flow. In terms of plant automation, the automatic 1-element control mode is required from the start-up of boiler. In this paper, the fuzzy controller is adopted for automatic 1-element control of drum level from start-up. It is suggested that the fuzzy controller is used in 1-element control, and the fuzzy-PI cascade controller is used in 3-element control. Finally, the validity of suggested control scheme is shown via simulation.

Lateral Information Propagation Neural Networks (LIPN) is proposed for on-line interpolation. The proposed neural network technique is the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. Information propagates among neighbor nodes laterally and inter-node interpolation is achieved. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed. 1-D LIPN hardware has been implemented with general purpose analog ICs to test the interpolation capability of the proposed neural networks. Experiments with static and dynamic signals have been done upon the LIPN hardware.

A lane change maneuver is a part of lateral control of an automated highway system. Assuming no direct measurement of its position during transition from one lane to another. A vehicle is controlled to follow the virtual desired trajectory using only on-board sensors. This paper investigates the development of a fuzzy controller for automated lateral control during emergencies. The performance of the fuzzy controller is presented at 20m/s for a step lane change and a double lane change. The robustness of fuzzy controller is investigated with respect to change in tire parameters and the number of passengers.

We have studied system identification model reduction method, optimal control by orthogonal functions. This paper presents the easy method that solves algebra equations instead of differential equations using Walsh, Haar, Block pulse function of orthogonal functions in state equation. The proposed algorithm is verified through some examples.

본 논문에서는 뉴로-퍼지 제어기를 이용한 능동 소음제어기를 구현하였다. 능동 소음제어기는 잡음에 의하여 왜곡된 신호로부터 잡음을 제거하여 원 신호를 복원하는 제어시스템이다. 일반적으로 잡음의 특성이 시간에 따라 변화하고, 전달특성이 비선형적이므로 고정된 제어기에 의해서는 제어할 수 없다. 이 논문에서는 뉴로-퍼지 제어기를 사용하였고, 파라미터를 오차 역전과 학습을 통하여 변화시킴으로써 잡음의 특성에 효과적으로 적응하는 능동 소음제어기를 구성하였다. 시뮬레이션을 통하여 여러 종류의 신호에 대해서 랜덤 노이즈를 발생시키고 구성된 제어기의 성능을 확인하였다.

In this paper, a multi variable neuro-fuzzy controller for a boiler-turbine system is designed. Two architectures are used. The first consists of boiler-turbine system identification and the second is designing a controller. A generalized backpropagation algorithm is developed and used to train the neuro-fuzzy controller. Designed controller is good tracking property and rejects the input and output disturbances. The results of the proposed design method is verified through simulation.

This paper presents the results of the field of an improved GPS navigation algorithm. The improved GPS navigation algorithm is a modified Kalman filter which is designed to be ideally suited to car navigation in urban area where lack of GPS visibility is the major problem because of the frequent blockage of the GPS signals by tall buildings and other structures. The method allows the user to estimate its position when the number of visible GPS satellites becomes less than four by using altitude fixing and clock bias estimation techniques. The two estimation techniques are integrated with the Kalman filter in a mutually compensating manner and it is shown that the 3-dimensional position accuracy is well maintained when the number of the visible satellites drops down to two for a reasonable period of time. The post processing results are included to show the improved performance of the modified algorithm over a normal conventional GPS Kalman filter.

Sliding mode is a robust control method and can be applied in the presence of model uncertainties and parameter disturbances. This study shows that the proposed fuzzy sliding mode control could reduce chattering problemed in sliding mode control. In this paper, an inverted pendulum is effectively controlled by the fuzzy sliding control technique. To reduce movable region of the inverted pendulum body, the angle and its integrated quantity are applied to the controller. The effectiveness of result is shown by the simulation and the experimental test for the inverted pendulum.

Neural Network has advantages of learning and normalizing capabilities. Fuzzy controller is based on a fuzzy logic that is so effective to represent uncertain phenomena of real world and make its approximation. In this paper, Fuzzy Neural Network controller which equipped with adaptive control algorithm is described. Proposed Fuzzy Neural Network Controller applied to a ball on beam system which have nonlinear characteristics shows a good performance.

In this paper, we introduced the neural network to reduce force ripple of current controller for a linear pulse motor. In general, conventional position controllers of linear pulse motor disregard the modeling error and load variations, which cause inaccuracy in position control. The proposed current controller based on neural network teaming modifies the current commands in order to reduce force ripple due to these factors. The experiment results show that the proposed controller works efficiently for accurate position control of linear pulse motor.

An intention of this paper is design of a gyro actuator for the attitude control of an unstructured object. It is well known that the attitude control of an object hanging with wire is not easy using usual actuators. Even though an actuator such as a pan can be used for control of the object, it is difficult to meet a desired control objectives. We, for this reason, propose a gyro actuator for the attitude control of an unstructured object. The proposed gyro actuator consists of two motors. The first motor is responsible to spin the wheel and the second motor is used to turn the outer gimbal. Appling the torque to the second motor, which results in the turn of the outer gimbal, torque about the vertical axis will be obtained while a wheel of the gyro is spinning constantly. This torque is used to control the attitude of the object attached. The aim of this paper is of deriving the transfer function of the actuator and presenting the guideline of the design parameters such as the weight and the dimension of the wheel, motors, and the load capacity. Simulations to the mathematical model which has a state feedback control are conducted to show the validity of the proposed gyro actuator.

Several sensorless vector control methods of induction motor have been proposed, but these methods don't have the satisfying performance to the change of the rotor time constant. Therefore, this paper proposes the sensorless vector control method which estimates the rotor speed using MRAS and compensates the rotor time constant using current error feedback at the same time. This method can guarantees the accurate performance of sensorless vector control while the rotor speed and the rotor time constant are changing. This method is verified by computer simulation using SIMULINK in MATLAB.

the satellite tracking problem of Antenna with two axis of elevation angle and azimuth one is described in this paper. The proposed control procedures for stabilization of nonlinear pedestal unit are consists of a off-line modeling identified by neural network and a on-line neural network controller combined with a reference model using the least square method. the simulation results are introduced and compared to a conventional PID controller.

This paper proposes position controller of linear motor using adaptive control algorithm. Some simulation results show the feasibility of the proposed controller for tracking control of linear motor.

The position control of an induction motor using Feedforward Neural Networks(FNNs) was studied in this paper. A teaching signal was obtained from sliding surface without a particular signal. And the FNNs team through the back propagation algorithm so as to reduce the error between the real position of the motor and the reference value. The structure of a controller was designed simply, for the fast calculating response which is certainly necessary for induction motor position control. And to show the superiority of this controller, 3-phase vector control induction motor whose power capacity is 2.2kw was modeled, and it was simulated.

In this paper, the optimal digital redesign is studied within the framework of fuzzy systems and dual-rate sampling control theory. An equivalent fast-rate discrete-time state-space model of the continuous-time system is constructed by using fuzzy inference systems. To obtain the optimal feedback gains developed in the continuous-time system, the constructed fuzzy system is converted into a continuous-time system. The developed continuous-time control law is converted into an equivalent slow-rate digital control law using the proposed digital redesign method. The digital redesign technique using a fuzzy model is employed to simulate the inverted pendulum dynamics.

Design of fuzzy logic controllers encounters difficulties in the selection of optimized membership function and fuzzy rule base, which is traditionally achieved by tedious trial-and-error process. In this paper We proposed a new method to generate fuzzy logic controllers throught genetic algorithm(GA). The controller design space is coded in base-7 strings chromosomes, where each bit gene matches the 7 discrete fuzzy value. The developed approach is subsequently applied to the design of proportional plus integral type fuzzy controller for a do-servo motor control system. It was presented in discrete fuzzy linguistic value, and used a membership function with Gaussian curve. The performance of this control system is demonstrated higher than that of a conventional PID controller and fuzzy logic controller(FLC).

The paper presents a model reference adaptive control containing a fuzzy algorithm for tuning the gain coefficient which adjusts the level of the fuzzy controller output. The synthesis of a fuzzy tuning algorithm has been performed for the inverted pendulum system. The computer simulation results have proved the efficiency of the proposed method, showing stable system responses.

This paper proposes a new hybrid algorithm for auto-tuning fuzzy controllers improving the performance. The presented algorithm estimates automatically the optimal values of membership functions, fuzzy rules, and scaling factors for fuzzy controllers, using a genetic-MGM algorithm. The object of the proposed algorithm is to promote search efficiency by a genetic and modified gradient optimization techniques. The proposed genetic and MGM algorithm is based on both the standard genetic algorithm and a gradient method. If a maximum point don't be changed around an optimal value at the end of performance during given generation, the genetic-MGM algorithm searches for an optimal value using the initial value which has maximum point by converting the genetic algorithms into the MGM(Modified Gradient Method) algorithms that reduced the number of variables. Using this algorithm is not only that the computing time is faster than genetic algorithm as reducing the number of variables, but also that can overcome the disadvantage of genetic algorithms. Simulation results verify the validity of the presented method.

Fuzzy inference systems have found many applications in recent years. The fuzzy inference system design procedure is related to an expert or a skilled human operator in many fields. Various attempts have been made in optimizing its structure using genetic algorithm automated designs. This paper presents a new approach to structurally optimized designs of FNN models. The messy genetic algorithm is used to obtain structurally optimized fuzzy neural network models. Structural optimization is regarded important before neural network based learning is switched into. We have applied the method to the problem of a time series estimation.

In this paper, the cross-coupling controller with fuzzy logic for AGV is developed, Cross-coupling control directly minimizes orientation' error by coordinating the motion of the two drive wheels and uses PI controller for compensation. But, the transient response of PI controller results in deviation from trajectory. The Fuzzy Cross-coupling controller enhances transient performance without steady-state error. The performance of the above controller is demonstrated by simulation and is compared with that of PI controller.

The fuzzy-based autonomous position control system for hovering of an unmanned helicopter has been developed. An unmanned helicopter Is flying vehicle which can aviate freely even at narrow or hazardous space. The bottleneck of the full utilization of the unmanned helicopter is mainly on the control difficulty caused from its nonlinear and unstable characteristics. This paper presents a Fuzzy control technique to have the unmanned helicopter perform hovering. Experimental results of real unmanned helicopter control are included.

This paper addresses analysis and design of a fuzzy model-based-controller for the control of uncertain SISO nonlinear systems. In the design procedure, we represent the nonlinear system by using a Takagi-Sugeno fuzzy model and construct a global fuzzy logic controller via parallel distributed compensation and sliding mode control. Unlike other parallel distributed controllers. this globally stable fuzzy controller is designed without finding a common positive definite matrix for a set of Lyapunov equations, and has good tracking performance. Furthermore, stability analysis is conducted not for the fuzzy model but for the real underlying nonlinear system. A simulation is included for the control of the Duffing forced-oscillation system, to show the effectiveness and feasibility of the proposed fuzzy control method.

This paper proposes the method that estimate optimally the parameters of Fuzzy-PID controller using genetic Algorithm. The controller is desined with the proposed method, and then is applied to 3-phase induction motor. Simulation results show that proposed method is more excellent then FPID and PID.

The helicopter system is non-linear and complex. Futhermore, because of absence of accurate mathematical model, it is difficult accurately to control its attitude. therefore, we propose a PID Neural Networks control technique to control efficiently its elevation angle and azimuth one. The coefficients of PID controller are automatically adjusted by the back-propagation algorithm of a neural network. The simulation results using MATLAB are introduced.

This thesis deals with the design problem of the state estimator for digital servo system. Digital servo system has input time delay, which depends on the size of control algorithm. The delayed input is a factor that brings out the state estimation error. So, in order to reduce this state estimation error of the system, we proposes a state estimator in which the delayed input of the system is considered. At first, a discrete-time state-space model is established accounting for the delayed input. Next, the state estimator is designed based on this model. we employ Kalman filter algorithm in design of the state estimator. The performance of proposed state estimator is exemplified via some simulations and experiment for servo system. And robustness of the proposed estimator to modelling error by variation of the system parameter is also shown in these simulations.

Generally, PI control is simple and easy to implement and gains of PI control are determined by specifying a dynamics of the servo driver system. However, the gain-tuning is so difficult that it is relied on an expert's effort. This paper presents a gain auto-tuning method for PI controllers based on a fuzzy inference mechanism. First, the proposed fuzzy inference system identifies a system moment of inertia and adjusts control gains by using the difference in speed responses between a real plant and a reference model. Second, this paper proposes an improved fuzzy PI controller. To reduce the speed overshoot, we adapt a control method that selects a proper PI gains with respect to the load inertia variation. To prove the validity of the proposed gain tuning algorithm and the feasibility of the servo drive, a high performance servo drive will be implemented by DSP(TMS320C31) and intelligent power module (IPM). The proposed controller is applied to the speed control of the 300W AC servo motor. Some simulations and experimental results show that the proposed fuzzy PI controller is more robust than the conventional PI controller against the load inertia variation.

In industry and engineering, the tensile measurement of single crystal metal material such as the uniform change, surface structure and the tensile torque of the material is not easy to obtain by current the tensile measurement methods. In this paper, we have implemented a tensile system which can acquire tensile information in real time.

초음파는 초음파 가공기나 초음파 세척기 등에 많이 이용되고 최근 액추에트로서 초음파 전동기가 주목을 받고 있다. 압전 혹은 자기비틀림 효과를 이용하여 얻는 초음파 진동기는 전기-기계 변환효율이 다른 수단에 비하여 매우 높으므로 이것을 이용하여 얻는 초음파 진동은 왕복운동에서 직선 혹은 회전운동으로 변환시키는 연구가 행해지고 있다. 본 연구에서는 진행파 방식의 초음파 전동기를 중심으로 그 회전 원리를 이해하고 모델링 하였다. 외란에 대하여 강인한 제어방식으로 알려져 있는 가변구조제어의 이론을 이해고 회전형 진행파 방식의 초음파 전동기를 이용한 가변구조, 제어에 의한 서보계를 설계한다. 이때 제어방법은 속도패턴을 미리 정하여 두고 그 패턴에 따라 슬라이딩 모드를 사용하여 속도를 제어하여 위치를 제어하는 것이다. 초음파 전동기의 가변구조제어에 의한 위치제어를 컴퓨터 시뮬레이션으로 검토하고 제어 파라미터를 결정하였다. 그리고 인터페이스 보드를 제작하고 컴퓨터와 전동기를 접속하여 실제 제어 알고리즘을 이용하여 초음파 전동기의 제어 실험을 행하였다.

In this paper, we design the optimal satellite-tracking antenna $H_{\infty}$ control system using genetic algorithm(GA). To do this, we give gain and dynamics parameters to the weighting functions and apply GA with reference model to the optimal determination of weighting functions and design parameter ${\gamma}$ that are given by Glover-Doyle algorithm which can design $H_{\infty}$ controller in the state space. These weighting functions and design parameter ${\gamma}$ are simultaneously optimized in tile search domain guaranteeing the robust stability of closed-loop system. The effectiveness of this satellite-tracking antenna $H_{\infty}$ control system is verified by computer simulation.

In target tracking problems the fixed gain Kalman filter is primarily used to predict a target state vector. This filter, however, has a poor precision for maneuvering targets while it has a good performance for non-maneuvering targets. To overcome the problem this paper proposes the system which estimates the acceleration with neural networks using the input estimation technique. The ability to efficiently fuse information of different forms is one of the major capabilities of trained multi-layer neural networks. The primary motivation for employing neural networks in these applications comes from the efficiency with which more features can be utilized as inputs for estimating target maneuvers. The parallel processing capability of a properly trained neural network can permit fast processing of features to yield correct acceleration estimates. The features used as inputs can be extracted from the combinations of innovation data and heading changes, and for this we set the two dimensional model. The properly trained neural network system outputs the acceleration estimates and compensates for the primary Kalman filter. Finally the proposed system shows the optimum performance.

The human chromosome analysis is widely used to diagnose genetic disease and various congenital anomalies. Many researches on automated chromosome karyotype analysis have been carried out, some of which produced commercial systems. However, there still remains much room for improving the accuracy of chromosome classification. In this paper, We proposed an optimal pattern classifier by neural network to improve the accuracy of chromosome classification. The proposed pattern classifier was built up of two-step multi-layer neural network(TMANN). We reconstructed chromosome image to improve the chromosome classification accuracy and extracted four morphological features parameters such as centromeric index (C.I.), relative length ratio(R.L.), relative area ratio(R.A.) and chromosome length(C.L.). These Parameters employed as input in neural network by preprocessing twenty human chromosome images. The experiment results shown that the chromosome classification error was reduced much more than that of the other classification methods.

A new approach to developing battery SOC indicator for electric vehicle is discussed in this paper. One of the most difficult problems associated with the development of electric vehicle is the battery indicator which reliably informs the state of charge(SOC) of the battery to the driver. And the condition to be satisfied with SOC indicator installed on the electric vehicle is that it should be used under frequently variable load. A new method to determining SOC using neural networks(NN) is proposed to satify the condition. The training data of NN are obtained by using mathematical model of lead-acid battery, and calculating discharge currents and terminal voltages while battery discharges with constant current. The 3-layered NN with back propagation algorithm is used Simulation results show that the proposed method is appropriate as SOC indicator of the battery.

In this paper, we are proposed optimization method of fuzzy model in order to complex and nonlinear system. In the fuzzy modeling, a premise identification is very important to describe the charateristics of a given unknown system. Then, the proposed fuzzy model implements system structure and parameter identification, using the fuzzy inference method and genetic algorithms. Inference method for fuzzy model presented in our paper include the simplified inference and linear inference. Time series data for gas furance and sewage treatment process are used to evaluate the performance of the proposed model. Also, the performance index with weighted value is proposed to achieve a balance between the results of performance for the training and testing data.

In this paper, we suggest a optimal design method of Fuzzy-Neural Networks model for complex and nonlinear systems. FNNs have the stucture of fusion of both fuzzy inference with linguistic variables and Neural Networks. The network structure uses the simpified inference as fuzzy inference system and the BP algorithm as learning procedure. And we use a clustering algorithm to find initial parameters of membership function. The parameters such as membership functions, learning rates and momentum coefficients are easily adjusted using the genetic algorithms. Also, the performance index with weighted value is introduced to achieve a meaningful balance between approximation and generalization abilities of the model. To evaluate the performance index, we use the time series data for gas furnace and the sewage treatment process.

In this paper, a new method of conrolling chaotic nonlinear systems is proposed. Firstly, the dynamics of a chaotic nonlinear system is separated into a linear part and a nonlinear part. Secondly, the nonlinear part is approximated using a radial basis function network (RBFN) and canceled from the controlled system. Then, the resulting system has only the linear part added with very weak nonlinearity. Finally, a simple linear state feedback control law is designed for the linear part. In the meanwhile, a theorem justifying this concept is presented and proved. Comparing with the feedback linearization, the proposed method can be applied regardless of the functional form of the controlled dynamics. The proposed method is applied by simulation to the Duffing system and the Lorenz system and satisfactory results are obtained.

This paper describes design method of control system with a pre-compensator using a neural network to compensate a error signal between a reference' signal and system response. The neural network which is used here is the mixed structure and it's algorithm is a back propagation that modify coupling coefficients. Applying this method to the position control system using DC servo motor as a driver, we verify the usefulness of this method with simulation.

In this paper, we propose a hybrid target tracking algorithm which combine the conventional Kalman filter algorithm and the fuzzy neural network. Conventional methods are degraded in the presence of uncertanties and the environmental noise. These problems can be resolved by the proposed method. The training data for the proposed target tracker is obtained by the off-line simulation. Unlike other target trackers usging fuzzy inference system, our method can be easily integrated into the existing system. A numerical simulation is included to show the effectiveness and the feasibility of the proposed method.

The paper presents an approach to the structure identification based on the CART (Classification And Regression Tree) algorithm and to the parameter identification by hybrid learning method in neuro-fuzzy system. By using the CART algorithm, the proposed method can roughly estimate the numbers of membership function and fuzzy rule using the centers of decision regions. Then the parameter identification is carried out by the hybrid learning scheme using BP (Back-propagation) and RLSE (Recursive Least Square Estimation) from the numerical data. Finally, we will show it's usefulness for fuzzy modeling to truck backer upper control.

This paper presents a scheme for fuzzy modelling and control of continuous-time nonlinear systems using a genetic algorithm. A fuzzy model is characterized by fuzzy "if-then" rules whose consequence part has a linear dynamic equation as subsystem of the system. The parameters of the fuzzy model are adjusted by a genetic algorithm. Then a tracking controller which guarantees stability of the overall system is designed. The simulation result demonstrates the effectiveness of the proposed method.

It is suggested that the PWM inverter is controlled by Digital Software Programming. VVVF(Variable Voltage Variable Frequency) inverter control being used by PWM control for driving the motor with speed-varying, makes the PWM pattern with calculating the output voltage and frequency, and with controlling the carrier and signal, so actually this method is difficult to correspond with driving the motor by using voltage-varying and frequency-varying. Therefore this research suggested the new algorithm controlled by micro processor which is already stored by various PWM form of output voltage by using fundamental data of the carrier and signal. The PWM wave can be controlled with real time by using extra hardware and digital software and to speed up program processing, the control signals to switch the power semi-conductor of three phase PWM inverter, simultaneously use the output signal by microprocessor and extra hardware, and control signal by software. In the end, this method was proved by applying to Three Phase Voltage-type Inverter.

In this paper, we present a robust adaptive backstepping output feedback controller for nonlinear systems perturbed by unmodelled dynamics and disturbances. Especially, backstepping technique with modular approach is used to separately design controller and identifier. The design of identifier is based on the observer-based scheme which possesses a strict passivity property of observer error system. We will use Switching-${\sigma}$ modification at the update law and the modified control law to attenuate the effects of undodelled dynamics and disturbances for nonlinear systems.

In the single-input and single-output system, the parameter of plant is scalar polynomial, but in the multiple input and multiple output, it accompanies, being matrix polynomial, the consideration of observable conrolability index or problems of non-commutation in matrix polynomial as well as degree, and it is more complex to deal with. Therefore, it is thought that a full reserach on the single-input and single-output system is not made. This reserach propose that problems of minimum variance self-tuning regulator of multivariable system and pole placement self-tuning regulator.

This paper deals with an algebraic compensator design for dynamic systems using a novel BPF transformation method. To obtain an algebraic compensator for the system, block pulse function's differential operation is used. Compare to unalgebraic compensator, proposed algebraic compensator is less sensitive to the measurement noise.

In this paper, a robust controller is proposed to achieve the accurate tracking for uncertain robot manipulators with hydraulic actuator dynamics. The parameter uncertainty can be quantified by the linear parameterization technique. A switching controller is proposed to guarantee the global asymptotic stability of the plant. In order to eliminate the chattering caused by the switching controller, a smoothing controller is proposed using the boundary layer technique around the sliding surface. It is shown that the smoothing controller guarantees the uniform ultimate boundedness of the tracking, error. The proposed controller shows good better tracking performance.

In this paper, Hadamard transform imaging spectrometers utilizing Grill spectrometers are proposed. General Hadamard Transform Spectrometers (HTS) carry out one-encoding through input masks, but Grill spectrometers carry out double-encoding through entrance and exit masks. Thus Grill spectrometers increase the signal-to-noise ratio by double-encoding. we reconfigure the system by using the Grill spectrometers which use a left cyclic S-matrix instead of the conventional right cyclic one. Then, we model the system and apply the mask characteristics method, i.e. $T^{I}$ method, to complete fast algorithm. Through computer simulations, we want to prove the superiority of the proposed system by comparing with the conventional HTS. From Observations concerning the average mean square error(AMSE) associated with estimates from the $T^{I}$ spectrum-recovery method, the relative performances of the two systems are compared.

As a basic study for Man-Machine interfacing technics, this paper purposed the vertorization of EOG(electrooculogram) that is generated by eye movement. EOG is electric potential difference between the positive potential of cornea and the negative potential of retina. The magnitude and the polarity are depend on the direction of eye movement and degree of gaze angle. In order to vectorize EOG, EOG signal is measured about vertical and horizontal movement of eyes. This vectorization of EOG is expected to help Man-Machine Interfacing technics and development of other useful equipment.

본 논문에서는 보급형 저차압 센서에 이용 될 수 있는 신호변환 회로를 설계하고 제작에 필요한 각각의 모쥴에 대한 특성 분석을 함으로서 그 성능을 파악하였다.

A structure of musculotendon model with a fatigue profile is investigated. The Hill-type musculotendon model can predicts the decline in muscle force for a given fatigue profile. It consists of nonlinear activation and contraction dynamics based on the physiological concepts. It is normalized for generalization to deal with the various muscles. Muscle force generated by continuous tetanic electrical monophasic pulsewidth modulation stimulation is decreased in time. A fatigue profile is expressed by a function of intramuscular acidification and applied to the relationship between muscle force and shortening velocity in contraction dynamics. The results of computer simulation are well matched with data in a literature which are isometrically performed for knee extension muscles. Also change in optimal fiber length has an effect only on muscle time, constant not on the steady-state tetanic force.

Control systems used in power plants consist of some control modules which are operated independently. A failure of control system mainly depends on a failure of control modules. So, it is important to estimate lifetimes of control modules in order to assess the reliability of control systems. In this paper, three methods for estimating lifetimes of control modules are presented and control modules' lifetimes of boiler control system used in a thermal power plant are estimated applying the three methods. Also, advantage and disadvantage of three methods are presented.

A PID-controller is proposed as a controller to the IFAC93 benchmark process. It is compared with a Textbook PID-controller and a Derivative of output PID-controller. Especially, the Derivative of output PID controller works within the critical bounds of ${\pm}1.5$ except for 1 out of 15 periods at stress level 1,2. The objective of this paper, then, is to report on an alternative benchmark (IFAC93) and reveal more efficient PID controller between Textbook PID-control and Derivative of output PID-controller.

This paper presents a method for estimating parameters of high frequency transient signals when noise is added. The parameters to be estimated are the magnitude, frequency, and decay rate of the signals. An approach based on only the extended Kalman filter (EKF) is highly dependent on choosing a correct value of variance of noise. The proposed method adopts an adaptive Kalman filter (AKF). Having very little information of the noise, This method avoids deterioration of the filter performance caused by choosing an inaccurate variance of the noise. The dependence of the EKF method upon the noise variance and the efficiency of the AKF method are shown.

This paper focuses on the fast convergence in nonlinear parameter optimization which is necessary for the fitting of nonlinear models to data. The simulated annealing(SA) and genetic algorithm(GA), which are widely used for combinatorial optimization problems, are stochastic strategy for search of the ground state and a powerful tool for optimization. However, their main disadvantage is the long convergence time by unnecessary extra works. It is also recognised that gradient-based nonlinear programing techniques would typically fail to find global minimum. Therefore, this paper develops a modified SA which is the SDS(Stochastic deterministic stochastic) algorithm can minimize cost function of optimal problem.

In this paper, Algebraic Model was proposed in order to overcome the large number of parameters to be adaptively modified is a drawback of the Genetic-Fuzzy control system. So, this problem has aggregating these parameters into a smaller number of parameters. this would result in an easier tuning procedure and would reduce the time necessary for computing the inputs to the process.

An adaptive joint-process IIR filter with generalized lattice structure is implemented by modifying the conventional lattice filter and making an adaptive algorithm in a system identification problem.

This paper presents experimental results of the fuzzy controller for DC servo motor. The fuzzy controller consists of 9 quantized levels and 25 fuzzy rules. The fine Controller is employed in the fine control mode when the value of error is between -0.03 and +0.03, whereas the coarse controller is used in the coarse control mode when the value of error is in the outside range of -0.03 and +0.03. The experimental results show that the fuzzy controller provides a better performance (lower overshoot and error) than the PID controller regardless of the load applied.

Based on wavelet theory, the new notion of wavelet networks is proposed as alternative to feedforward neural networks for approximating arbitrary nonlinear functions. An algorithm presented in this paper trains coefficients of wavelet. i.e., translations and scaling., and then learns weights with the wavelet coefficients. And experimental results are reported.

This paper discusses approximation modelling of discrete-time linear time-varying system(LTVS). The wavelet theory and genetic algorithm are considered as a tool for representing and approximating a LTVS. The joint time-frequency properties of wave analysis are appropriate for describing the LTVS. Simulation results is included to illustrate the potential application of the technique.

This paper deals with the stability of discrete time linear systems with time - varying delays in state. In this paper, the magnitude of time - varying delays is assumed to be upper-bounded. The stability of discrete time linear systems with time - varying delays in state is related with the stability of discrete time linear systems with constant time delay in state. To show this, a new Lyapunov function is proposed. Using this Lyapunov function, a sufficient condition for the asymptotic stability is derived.

The main drawback of filtered-X LMS(FXLMS) algorithm for the ANC of broadband noises is its low convergence speed when the filtered reference signals are strongly correlated, producing a large eigenvalue spread in correlation matrix. This correlation can be caused either by autocorrelation of the signals of the reference sensors, or by coupling between the error path which introduces intercorrelation in the filtered reference signals. In this paper, we introduce a transform domain FXLMS(TD-FXLMS) algorithm that has a high convergence speed by orthogonal transform's decorrelation properties.

This paper describes a robust stabilization of single input nonlinear systems with parametric uncertainty. We first investigate differential flatness of the nominal nonlinear systems. If a single input system is differentially flat, it possesses a flat output. And we define coordinate transformation functions via successively differentiating the flat output, and we also consider the robust fictitious controls at every differentiation of the flat output. In the new coordinates the nonlinear system is transformed into the Brunovsky normal form with matched uncertainty. With a robust control based on the Lyapunov method, the robust stabilization is achieved.

In this paper, when we control Elevation Angle and Azimuth Angle of Antenna, intend to implement sensor system for stabilization control of antenna pedestral system because of wind in land, wave and external disturbances such as rolling, pitching, and yawing. Therefore, this sensor system is consist of Tilt Sensor for measuring absolute angle of roll ing and pitching, Level Rate Sensor, Cross Level Rate Sensor, Azimuth Rate Sensor for controlling short_term azimuth angle and Flux Gate Sensor for measuring long_term azimuth angle.

This paper deal with the implementation of Active Noise Control (ANC) in a short duct. In case of ANC in the air duct, input microphone, control speaker, error microphone are used. But we can't use input microphone because of the characteristics of short duct. It is difficult to avoid howl. So we propose single-channel adaptive feedback ANC which is composed only error microphone and control speaker without input microphone. FXLMS algorithm is used to compensate for the time delay of the error path. Experimental results show that the controller reduce noise signal sufficiently.

In this paper we discuss an algorithm to discriminate a target under track against multiple acoustic counter-measure (ACM) sources, based on sequential testings of multiple hypotheses. The ACM sources are separated from the target under track and generate, while drifting, measurements with false range and Doppler information. The purpose of the ACM is to mislead the target tracking and to help the true target evade a pursuer. The proposed algorithm uses as a test statistic a function of both the sequences of processed waveform signature and the innovation sequences from extended Kalman filters to estimate the target dynamics and the drifting positions of the ACM sources. Numerical experiments on various scenarios show that the proposed algorithm discriminates the target faster with a higher probability of success than the algorithm using only the innovation sequences from extended Kalman filters.

실장 PCB에 대한 검사업무는 많은 인력과 시간비용을 요구 제품의 생산성을 저하시킴으로써 제품에 대한 경쟁력 확보에 큰 장애요인이 되고 있다. 따라서, 기업들은 검사 자동화를 추진하여 왔는데, 검사 생산성을 획기적으로 개선하기 위해서는 검사패턴을 자동 생성하고 아날로그 회로나 디지털 회로상의 전자 소자나 회로 기능을 선택적으로 검사함으로써 검사비용을 최소화할 수 있는 다기능 검사 기능이 요구된다. 따라서, 본 연구에서는 회로 보오드상의 부품 특성에 따라 최적의 검사패턴을 자동 작성하고 동시에 실행할 수 있는 지능형 검사 시스템을 개발하고자 한다.

The measuring data acquisition methods from local sensor are many kinds of techniques. But, if sampling time is not important and we need data of many sensors it is more resonable to be applied economical system. In this paper, the data acquisition technique is used two RS232C communication signals simultaneously. The one come from computer serial port, and another is signal changed from parallel port. In this case the circuits would be simplification and that communication cable is connected by Parallel instead of Branch connection.

An adaptive nonlinear observer-based longitudinal control law for vehicles is presented in this paper. It is assumed that for vehicle i knows only the distance between vehicle i and the preceding vehicle, i-1. An adaptive nonlinear state observer for vehicle i is developed to estimate the velocity and acceleration of the preceding vehicle, i-1. The communication of the position, velocity, and acceleration information is not used in the proposed method. It will be shown by mathematical analysis that the longitudinal control of vehicle can be implemented without an communication of the informations. It will be proven that the observation errors of the nonlinear states converge to zero asymptotically. To show the effectiveness of the proposed method, the simulation results are presented for the longitudinal control of the vehicle.

Engine immobilizer is the automobile security system which disables the engine if the secrete code in the transponder embedded in the key knob is not in agreement with the code in ECU of the car. There are many types of immobilizer systems, however, the encryption transponder type system is the most secure system due to the code verification method using an encryption method. As an example of the industry-university cooperation, the software development in the system is introduced in this paper.

Inside keys for the automobiles are the keys whose key teeth are engraved on the inside of the keys. These types of the keys are very effective in prohibiting making the copies of the keys for the criminal purposes and have excellent mechanical strenth comparing with the ordinary types of the keys. In this paper, a development of the control system for the milling machine which is used for cutting the teeth of the inside keys. This machine is controlled by a computer and the cutting is done automatically according to the key codes which are contained in the key code file. This work is presented to show an example of the industry-university cooperation.

In this paper, a development of the control system for the milling machine which is used to cut teeth of the automibile keys is presented as an example of the industry-university cooperation. The machine is controlled by a computer and a PLC. The control of the servo motors are accomplished by the computer and mechanical sequences are by PLC. This machine is capable of cutting key teeth up to the accuracy of 10 microns.

Based on fuzzy logic algorithm this paper constructed fuzzy logic controller for automated vehicles. For passenger's convenience especially comfortability controller need to reduce the frequency of input variable's changing. So we established membership functions for comfortability as mil as speed following. It made possible to control comfortability directly. To demonstration the efficiency of fuzzy logic controller, we carried out simulation with a Automobile's transfer function. First, we designed the PID controller by using Ziegler-Nichols tunning method. Second, we calculated time response for each controller, then we compared the speed patterns of fuzzy controlled system and PID controlled system. Also we compared the difference of input variable. By comparing two controller's response, we can confirm the merit of fuzzy controller about comfortability. Fuzzy controller can reduce input changing frequency.

Fuzzy Inference System is to trans late and be concrete with human expert in to mathematical equation. It is easy to be applied for Nonlinear System and the know ledge can be applied at that. With using the rule according to the Knowledge, when it is realized simulations must be required repeatedly and small vibration is generated in steady state, too. In this paper, we applied the system to the methodology of optimization with self-learn ing by us ing ANFIS(Adaptive Network-based Fuzzy Inference System) which makes use of back-propagation and least square method at a first order Sugeno Fuzzy System. In order to show the effect of Algorithm, we demonstrated it by us ing Rotary Inverted Pendulum.

This paper consider fuzzy control of a single-inverted pendulum attached to the tip end of a rotating arm driven by a direct driven motor. Control objectives stabilization of the pendulum at the upright position and regulation of the arm at an arbitrary specified position. Fuzzy control is an effective method to achieve multiple control objectives in control of nonlinear systems. In this paper, fuzzy logic control is proposed to obtain increased control performance and stability.

This paper represents fundamental developments in Fuzzy and Neural approaches. The Fuzzy Controller(FC) and plant are cascaded in Adaptive framework. Each of which produces its outputs. The adjustable parameters all pertain to the fuzzy controller is implemented as an Adaptive FC to adjust the environments of the plant. There is an error meaure block which is a difference between the actual state and desired state. We introduce error back propagation algorithm in neural method. To speed up convergence, we follow a steepest decent in the sense that each parameter set update leads to a smaller error measure and is learned by this methodology. Inverted pendulum is a typical testbed to measure the effectiveness of nonlinear control system. finally we simulated the adaptive fuzzy controller to be able to bring back to the upright position of the its angle and angular velocity.

In general, fuzzy control system are efficient for the systems which are complicated and nonlinear. But the fuzzy control flawed by the fact that it is much trial and errors in process of getting parameters of membership function which can express optimal status of system. This paper shows the methodology which is applied of ANFIS(Adaptive Neuro-Fuzzy Inference System) for the coverage against these defects. It proved superiority of ANFIS by controlling inverted pendulum.

Fuzzy controller design consists of intuition, and any other information about how to control system, into a set of rules. These rules can then be applied to the system. It is very important to decide parameters of IF-THEN rules. Because fuzzy controller can make more adequate force to the plant by means of parameter optimization, which is accomplished by learning procedure. In this paper, we apply fuzzy controller designed to the inverted pendulum.

Fuzzy controller design consists of intuition, and any other information about how to control system, into a set of rules. If the parameters of membership function in premise part and consequent part are set adequately, the controller designed can control plant well. But, if the parameters of function are set inadequately, the controller can't control well. So we must modify parameters using adaptive learning procedure. In this paper, we design adaptive fuzzy controller, and then verify its robustness.

This study investigates the population model of the spread of HIV/AIDS which the infection is generated by an infectious in dividual in a population of susceptibles. A mathematical model is presented for the transmission dynamics of HIV infection within the communities of homosexual males. The pattern on the epidemic character of HIV, the causative agent of AIDS, was analysed by the mathematical model of AIDS system which is derived according to the ecological relationship between five epidemilogic states of individuals. The computer simulation was performed using real data.

In this paper, we have suggested the method of genetic algorithm to solve the trajectory optimization. The given nonlinear method is so complex and modeling is not easy. Also, we have suggested the nonlinear programming combined with genetic algorithm. The proposed algorithm gives simple and time-reducing method in solving nonlinear dynamic systems.

Recently, it was very difficult for hydraulic governor to regulate the speed of high power engine with long stroke at low speed and low load, because of the jiggling phenomena by rough fluctuation of rotating torque and the hunting phenomena by long dead time occurred in fuel combustion process in the engine cylinder. In this paper, the influence of engine dead time is investigated by Nickels chart, and hybrid controller selected advantages of PID and fuzzy logic controller is provided to improve the performance of speed control of a low speed and long stroke diesel engine.

본 연구에서는 도로 교통 정보검출을 위하여 사용되는 검지기 가운데 비매설형 차량검지 기술에 대한 분석을 통하여 국내환경에 적합한 검지기술의 기본 기능을 설정하였다. 분석된 검지기는 15개의 국내외 검지시스템으로 검지 성능 분석과 국내 적용시 발생되는 문제점을 파악하여 설계시 필요한 기본 계획을 설정하였다.

Fuel cell systems offer high energy efficiencies for transportation application. In addition, they can use alcohols and alternative fuels as the fuel, while producing little or no noxious emissions. Fuel cell-powered vehicles should be competitive in performance characteristics and in capital and maintenance costs with internal combustion engine vehicles. The objective of the present study is to design a fuelcell/battery powered vehicles to analyze technical feasibity.

There are a lot of paths which connect between the mobile robot and the goat point. To make a mobile robot arrive at the goal point fastly, The optimal path is needed and a path palnning is necessary. In this paper, we propose a new method of path planning to find a path for mobile robot. It is based on Ginetic Algorithm for serching the optimal grobal path planning. Simulations show the efficiency for the grobal path planning.

In this paper, we reviewed the steam temperature control in an once through boiler. The steam temperature control is very difficult. Generally, steam temperature of an once through boiler is not only controlled by boiler spray water flow, but also influenced by feed water flow and fuel flow. An advanced control strategy has been developed by experienced engineer. Specifically, We reviewed temperature control strategy for Taian power plant in this paper. This control strategy is represented by state control observer. This state control observer algorithm for temperature control has been used since the late 1980's. This paper describes control strategy employed and observed benefits from advanced steam temperature control.

One of the major concerns of our time is the need to use energy economically and rationally while at the same time, protecting the environment. Circulating Fluidized Bed(CFB) Boilers represent a proven, very attractive clean coal technology, with the added advantage of an unusual fuel flexibility CFB boiler is the best available compromise between cost and environment for fossil fuel power plant. This paper briefly describes CFB process and 200MW CFB boiler for Tonghae power plant. Also, discussed are differences between the control process of fluidized bed and conventional boilers, and applied control process for Tonghae power plant.

It is difficult to determine the controller parameters that we can get optimum response of the controlled process variable. In this paper we investigate the effects of various elements of which a gas turbine MW control loop is consists. And we describe the result of actual adjustment on the parameters of these elements to improve the speed regulation of a gas turbine.

The control valve positioner is a high gain plain proportional controller which measures the valve stem position and compares it to its setpoint which is the primary controller output. The positioner in effect is the cascade slave of the primary controller. In order for a cascade slave to be effecttive, it must be fast enough compared to the speed of its set point change. This paper describes the positioner transfer function and its effect on the entire control loop characteristic based on the simulation results. The result showed that the control valve and positioner determined the gain and phase angle in the high frequency range, while the primary controller and process determined those of the low frequency range. We can also anticipate the combined characteristics in the whole frequency range when each element's frequency response is known.

PID controllers are widely used in the process industries such as power plants and chemical plants. Several methods for determining PID controller parameters have been suggested to improve tuning results by various performance criteria during the past years. These methods may not produces satisfactory closed loop response by the characteristics of controlled processes. In this paper, using a model of gas turbine system obtained by operating data of Gunsan C/C, we examines the performance of PI controllers determined by various performance criteria and suggests which tuning methods can be optimally used in gas turbine control system of Gunsan C/C.

In this paper we obtain a discrete mathmatical model of a Gas turbine control system from experimental data. we find appropriate input signal and parameter estimation algorithm for identification of the gas turbine control system. Under these conditions experimental data are collected from real system and parameters are estimated by the recursive least square algorithm. The computer simulation results show that the proposed experimental procedure is appropriate for the identification of the gas turbine control system. The model validation is excuted by real data from the Gunsan Gas Turbine Power Plant.

This presentation describes an experiment to intergrated control, protection and measurement system configulation at KOWACO's Yongdam hydro-power station currently constructed by discrete electronic and electromechenical devices. The experiment is designed to exploit existing microcomputer technologies, digital signal processors, m/w&fiber optic communications. The theory of operation and advantages of the intergrated approach are discussed.

This paper presents an optimization algorithm for a stable Dynamic Neural Network (DNN) using genetic algorithm. Optimized DNN is applied to a problem of controlling nonlinear dynamical systems. DNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. The real time implementation is very important, and thus the neuro controller also needs to be designed such that it converges with a relatively small number of training cycles. SDNN has considerably fewer weights than DNN. The object of proposed algorithm is to the number of self dynamic neuron node and the gradient of activation functions are simultaneously optimized by genetic algorithms. To guarantee convergence, an analytic method based on the Lyapunov function is used to find a stable learning for the SDNN. The ability and effectiveness of identifying and controlling, a nonlinear dynamic system using the proposed optimized SDNN considering stability' is demonstrated by case studies.

In this paper, a new sliding mode observer is proposed by introducing a new sliding surface. The new sliding surface is defined based on the augmented error system with virtual error state. The new sliding mode observer have more degree of freedom than the existing VSS observer. It can have dynamics on the sliding surface.

A new $H_{\infty}$ robust controller is proposed by using Sliding Mode Control (SMC). The combination of $H_{\infty}$ with SMC is achieved by proposing a novel sliding surface which has a virtual state. This sliding surface has the nominal dynamics of an original system controlled by $H_{\infty}$ controller. Its design is based on the augumented system whose dynamics have one higher order than that of the original system. The reaching phase is removed by setting an initial virtual state which makes the initial sliding function equal to zero.

This paper gives a simple parameterization of all stable unbiased filters to solve the suboptimal mixed $H_2/H_{\infty}$ filtering problem. Using the central filter, mixed $H_2/H_{\infty}$ filter is designed which minimizes the upper bound for the $H_2$ norm of the transfer matrix from a white noise to the estimation error subject to an $H_{\infty}$ norm constraint on the transfer matrix from an energy-bounded noise to the estimation error. The problem of finding suitable estimator gain can be converted into a convex optimization problem involving linear matrix inequalities.

In this paper the control methods for the synchronous generator is designed based on the two-degree-of-freedom (TDOF) control methods which can safisty the command following property and Robust control property at the same time. The power systems is reduced to one machine infinite-bus system. Robust stability of the proposed power system stabilizer is checked through the simulation considering the circumstance which can happen in real situation.

This paper proposes a new high-voltage measuring system that is able to provide current enough to charge a storage battery for a motor driving a load switch, and detect each phase voltage and phase angle in three phase distribution line accurately. In order to develop the reliable voltage measuring system we had designed and manufactured condenser-type bushing having good characteristics for temperature on account of using epoxy resin as dielectric material, and investigated the variation characteristics of capacitance via variation of applied voltage. The experiment results show that the proposed condenser has good characteristics to environmental changes.

Due to the reinforcement of government's DSM(Demand Side Management) policy. Solid State Meter was introduced in Korea since 1993 and it is applied to the high voltage customer exceeding 100kW in order to equalize daily load curve. In recent days, KEPCO has a Plan to use the Solid State Meter which has a data recording and remote meter-reading function for low voltage customer to introduce the real-time pricing system and reduce peak power in the near future. So, this paper suggests the specification and function of Solid State Meter.

Flicker embrace all diverse effects of rapid voltage fluctuation. To minimize these effects, the voltage fluctuation must be kept below the limit. The aim of this study is to simulate TCR for flicker suppression. To do this, we built models similar to electrical systems of fields using EMTP and attached TCR to them. After attaching TCR, we confirmed reduction of incoming voltage fluctuation and reactive power compensation.

Optical Temperature Distribution measurement System (OTDS) is completely different from conventional electric point sensor in that it uses the optical fiber itself as the sensor. This new concept in temperature measuring system requires only one fiber to be laid. The use of optical fiber also gives the advantage of small diameter, light weight, explosion resistance, and electromagnetic noise resistance. The OTDS is a sensor which is capable of making a precise measurement over a wide range of areas using only a single optical fiber. Since current temperature sensors, such as the thermocouple, are only used to measure temperaturea of point, they are almost impractical for measuring a wider range because of the extremely high cost. In comparision with current sensors, the optical fiber distributed temperature sensor can make much quicker and more precise measurements at a comparatively low cost.

본 논문에서는 지방국도에 시설되고 있는 교통 정보 제공시스템의 운전 상태를 감시하여 보수와 함께 유지관리를 수행하기 위한 종합 보수지원 시스템을 구현한다. 제안된 시스템은 국도상에 불규칙하게 설치되는 차량검지 시스템을 계층 집중형 감시시스템으로 디자인하여 실시간 감시와 함께 발생되는 운전 이력과 정보를 활용하여 유지보수 체계의 온라인화를 실혐하였다.

MMIS System of KNGR is composed of systems by using of digital equipment in general, and also interface function among systems is completed by a network. According to this, KNGR PCS also got rid of many kinds of interface cards which have been used for hardwired interface to outside system, and most of function in these cards is to be programmed by PLC. This paper defines the function and method which is to be programmed to PLC. And this paper presents new function which is to be added for operator's interface by using network. It is expected that PCS logic cabinet will be more simplified, satisfy KNGR design concept, and operation convenience will be increased.

The Ziegler-Nichols parameter tuning has been widely known as a fairly heuristic method to good determine setting of PID controllers, for a wide range of common industrial processes. We extract process knowledge required for rule base controller through tuning experiment and simulation study, such as set point weighting and normalised gain and dead time of process. In this paper, we presents a rule base PID controller by extracted process knowledge and the modified Ziegler-Nichols tuning. Computer simulation are provided demonstrate the feasibility of this approach.

The distributied control system consists of PCS(Process Control System), OIS(Operator Interface System), Communication Network and EWS(Engineering Workstation). This parer describes the system structures and the technological trends of domestically developed distributied control system (DCS).

Since a gas turbine is still a significant contributor to peak time, it is very important to tune the gains of P. I. D to get a maximum power and stability within permissible limits. In the gas turbine, the main control loop must adjust the fuel flow to ensure the correct output power and frequency. but it is not easy, because the control loop is composed of many subsystems. In this paper we acquire a transfer function based on the operations data of Gun-san gas turbine and study to apply a loop compensation type 2-DOF PID controller tuning by neural-network to control loop of gas turbine to reduce phenomena caused by integral and derivative actions through simulation. We obtained satisfactory results to disturbances of subcontrol loop such as, fuel flow, air flow, turbine extraction temperature.

본 논문에서는 ${\alpha}$, ${\beta}$분리 및 결합형, 피드 포워드형, 미분선행형(PI형)의 2-자유도 PID제어기의 각 종류에 대해 파라메터 변화에 따른 특성을 가스 터빈의 연료 제어계통 전달함수를 이용해 비교 고찰하였다. ${\alpha}$, ${\beta}$는 제어기의 출력 특성에 크게 영향을 미치나 ${\gamma}$는 크게 영향을 미치지 않는 것으로 나타났고 각 제어기의 특성에서는 ${\alpha}$, ${\beta}$결합형이 가장 양호한 출력 특성이 나타났다.

As the modern industrial processes become more complex, it is getting more difficult to model and control the processes. The PID controller has been widely used in power plant process control since its structure is simple and familiar to the field engineer. This paper describes the application of the PID controller developed DCS system in power plant.

This paper suggests a design method of the optimal PD control system having robust performance. This PD control system is designed by applying genetic algorithm(GA) with reference model to the optimal determination of proportional(P) gain and derivative(D) gain that are given by PD servo controller. These proportional and derivative gains are simultaneously optimized in the search domain guaranteeing the robust performance of closed-loop system. This PD control system is applied to the fuel-injection control system of diesel engine and compared with ${\mu}$ -synthesis control system for robust performance. The effectiveness of this PD control system is verified by computer simulation.

In recent years, advances in construction techniques and materials have given rise to flexible light-weight structures. Because these structures extremely susceptib environmental loads, these random loadings u produce large deflection and acceleration on structures. Vibration control system of structur becoming an integral part of the structural syst the next generation of tall building. The proposed control system is applied to s degree of structure with mass damping and com with conventional PID and neural network PID system.

가압경수형 원전에서 증기발생기의 수위제어가 저출력하에서 유체거동이 부정확하고 비정상적이어서 기존의 PI제어기 만으로는 파라메타 설정이 곤란하여 효과적인 제어가 어렵다. 이러한 문제점을 개선하고자 인공지능기법의 일종인 신경회로망을 이용한 수위제어 알고리즘의 적용을 연구하였다. 저출력시에는 증기발생기내에서의 물리적인 현상이 상당히 복잡하여 정확한 수학적 모델링이 어렵기 때문에 기존의 PI제어기와는 별도로 입출력신호패턴에 근거한 수위변동의 경향인식으로 요구되는 수위레벨을 과도현상없이 안정적으로 제어 할 수 있었다. 이 연구결과에 기초하여 저출력시에 한하여 신경회로망을 적용한 컴퓨터로써 병렬운전을 수행한다면 효과적인 현장적용성을 높일 수가 있다.

Most of Instrumentation and control system in Nuclear Power Plant using analog equipment has a limitations in system designing and operation. So the network based PLC application to Nuclear Power Plant has been studied so far. This paper defines the configuration and method with Prototype which are to be applied to NPCS(NSSS Process Control System) of KNGR (Korea Next Generation Reactor). It is expected that Test result of NPCS Pototype will satisfy the essential requirements in designing KNGR.

Plant Protection Systems(PPS) are those systems that initiate safety actions to mitigate the consequences of design basis events by sending signals to Reactor Trip Switch Gear System(RTSS) and Engineered Safety Features-Component Control Systems(ESF-CCS). This paper illustrates distinctive features and improved design concepts of Korea Next Generation Reactor(KNGR) based on the experience obtained through prototyping of PPS.

Analog I&C hardwares in most of nuclear power plants in operation are aging since they were installed 1950's or 1960's. Thus, the replacement of such systems with digital computer-based systems is currently a new trend in nuclear area. This paper will discuss such transition occurring in nuclear I&C area, Comparative study on the digital technique adopted in leading nuclear industries. Its advantage and performance evaluation will be illustrated as well.

Engineered Safety Features-Component Control Systems(ESF-CCS) are those I&C systems that control safety equipment used to maintain the integrity of reactor coolant pressure boundary. This paper illustrates distinctive features and improved design concepts of Korea Next Generation Reactor(KNGR) based on the experience obtained through prototyping of ESF-CCS.

In this paper, a highly reliable communication network for DCS (Distributed Control System) in nuclear power plant is designed. The structure and characteristics of DCS in nuclear power plant is briefly explained. The features needed for a communication network for DCS in nuclear power plant is described. According to the above features, a layer structure for the communication network is determined and each layer is designed in detail. Accuracy of the model was evaluated by computer simulation.

This paper deals with a method or a residual generation for fault isolation in a centrifugal pump with a water circulation system, driven by a speed controlled dc motor. It is based on parity relations derived from the moving-average model of the system and is used to identify sensor faults and two possible brush and impeller faults, where the former is dealt with additive faults, while the latter characterized as discrepancies between the nominal and actual plant parameters of the system is modelled by multiplicative faults. We will represent the propagation of this uncertainty to the model matrices by the approximate handling of partial derivatives of polynomials. With multiplicative faults, the transformation matrix implemented in the residual generator are calculated on-line. The simulation studies demonstrate that small changes of the system can be detected and diagnosed by using the method.

The control of indoor illumination using fuzzy controller and sunlight is presented. Indoor illumination 220[lx] can be kept according as blind control with sunlight.

Air pollution is one of the most important global issues of the environmental concerns. Some advanced foreign countries have developed the air pollution control technology. KEPCO has been researching on the air pollution control technology and developed the FGD(Flue Gas Desulfurization) system for 200MW thermal power plant. In this paper, we describe the major control loops implemented to the domestic FGD system. The major control loops are to be classified into booster fan control, absorber PH control and limestone density control. The control loops were applied to the actual desulfurization processes and proved to their performance.