• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.1
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• pp.52-64
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• 1995

Robot manipulator is a highly nonlinear-time varying system. Therefore, a lot of control theory has been applied to the system. Robot manipulator has two types of control; one is path planning, another is path tracking. In this paper, we select the path tracking, and for this purpose, propose the intelligent control¬ler which is combined with fuzzy logic and neural network. The fuzzy logic provides an inference morphorlogy that enables approximate human reasoning to apply to knowledge-based systems, and also provides a mathematical strength to capture the uncertainties associated with human cognitive processes like thinking and reasoning. Based on this fuzzy logic, the fuzzy logic controller(FLC) provides a means of converhng a linguistic control strategy based on expert knowledge into automahc control strategy. But the construction of rule-base for a nonlinear hme-varying system such as robot, becomes much more com¬plicated because of model uncertainty and parameter variations. To cope with these problems, a auto-tuning method of the fuzzy rule-base is required. In this paper, the GA-based Fuzzy-Neural control system combining Fuzzy-Neural control theory with the genetic algorithm(GA), that is known to be very effective in the optimization problem, will be proposed. The effectiveness of the proposed control system will be demonstrated by computer simulations using a two degree of freedom robot manipulator.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.10a
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• pp.93-93
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• 1998

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.106-116
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• 1998

A position control algorithm of a flexible manipulator is studied. The proposed algorithm is based on an ACFAC(Automatic Constructed Fuzzy Adaptive Controller) system based on the neural network learning algorithms. The proposed system learns membership functions for input variables using unsupervised competitive learning algorithm and output information using supervised outstar learning algorithm. ACFAC does not need a dynamic modeling of the flexible manipulator. An ACFAC is designed that the end point of the flexible manipulator tracks the desired trajectory. The control input to the process is determined by error, velocity and variation of error. Simulation and experiment results show a robustness of ACFAC compared with the PID control and neural network algorithms.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.215-218
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• 1996

ATM망은 실제로 이용 가능한 대역폭 이상을 할당하는 통계적 다중화(statistical multiplexing) 기법을 사용하므로 망을 통한 트래픽 흐름을 적절히 관리하지 못하면 혼잡(congestion), 셀 손실, 망의 성능 저하 등을 야기하게 된다. 이러한 상황을 예방하고 셀의 도착 시간 버스트(burstiness)를 줄이며 셀 손실 특성을 개선하여 망의 성능을 증가시키기 위하여, 트래픽의 형태 제어 방법을 제안한다. 트래픽 형태 제어 파라미터 값의 역전파 신경망을 적용하여 예측되며, 이 예측된 값들에 의해 형태 제어 방법을 수행한다. 제안된 형태 제어 기법의 성능은 Poisson 트래픽 입력에 대한 컴퓨터 시뮬레이션에 의해 얻어지며, 멀티플렉서에서의 최대 버퍼 크기를 측정하여 성능을 평가하였다.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.14 no.3
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• pp.49-58
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• 2000

This paper deals with a systematic approach to neural network controller design for static VAR compensator (SVC) using a learning algorithm of error back propagation that accepts error and change of error as inputs, the momentum learning technique is used for reduction of learning time, to improve system stability. A SVC, one of the Flexible AC Transmission System(FACTS), constructed by a fixed capacitor(FC) and a thyristor controlled reactor(TCR), is designed and implemented to improve the damping of a synchronous generator, as well as controlling the system voltage.TO verify the robustness of the proposed method, we considered the dynamic response of generator rotor angle deviation, angular velocity deviation and generator terminal voltage by applying a power fluctuation and rotor angle fluctuation in initial point when heavy load and normal load. Thus, we prove the usefulness of proposed method to improve the stability of single machine-infinite bus with SVC system.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2001.05a
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• pp.243-248
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• 2001

The stabilization control of Inverted Pendulum(IP) system is difficult because of its nonlinearity and structural unstability. Thus, in this paper, an Evolving Neural Network Controller(ENNC) without Error Back Propagation(EBP) is presented. An ENNC is described simply by genetic representation using an encoding strategy for types and slope values of each active functions, biases, weights and so on. By an evolutionary programming which has three genetic operation; selection, crossover and mutation, the predetermine controller is optimally evolved by updating simultaneously the connection patterns and weights of the neural networks. The performances of the proposed ENNC(PENNC) are compared with the ones of conventional optimal controller and the conventional evolving neural network controller(CENNC) through the simulation and experimental results. And we showed that the finally optimized PENNC was very useful in the stabilization control of an IP system.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.720-729
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• 2005

In this paper, we design the indirect adaptive controller using Wavelet Neural Network(WNN) for unknown nonlinear systems. The proposed indirect adaptive controller using WNN consists of identification model and controller. Here, the WNN is used in both Identification model and controller The WNN has advantage of indicating the location in both time and frequency simultaneously, and has faster convergence than MLPN and RBFN. There are several training methods for WNN, such as GD, GA, DNA, etc. In this paper, we present the Extended Kalman Filter(EKF) based training method. Although it is computationally complex, this algorithm updates parameters consistent with previous data and usually converges in a few iterations. Finally, ore illustrate the effectiveness of our method through computer simulations for the Buffing system and the one-link rigid robot manipulator. From the simulation results, we show that the indirect adaptive controller using the EKF method has better performance than the GD method.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2001.10a
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• pp.91-96
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• 2001

This paper presents a neural net based nonlinear adaptive controller for an autonomous underwater vehicle (AUV). AUV's dynamics are highly nonlinear and their hydrodynamic coefficients vary with different operational conditions, so it is necessary for the high performance control system of an AUV to have the capacities of learning and adapting to the change of the AUV's dynamics. In this paper a linearly parameterized neural network is used to approximate the uncertainties of the AUV's dynamics, and a sliding mode control is introduced to attenuate the effects of the neural network's reconstruction errors and the disturbances of AUV's dynamics. The presented controller is consist of three parallel schemes; linear feedback control, sliding mode control and neural network. Lyapunov theory is used to guarantee the asymptotic convergence of trajectory tracking errors and the neural network's weights errors. Numerical simulations for motion control of an AUV are performed to illustrate to effectiveness of the proposed techniques.

• Journal of Ocean Engineering and Technology
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• v.16 no.1
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• pp.8-15
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• 2002

This paper presents a neural net based nonlinear adaptive controller for an autonomous underwater vehicle (AUV). AUV's dynamics are highly nonlinear and their hydrodynamic coefficients vary with different operational conditions, so it is necessary for the high performance control system of an AUV to have the capacities of learning and adapting to the change of the AUV's dynamics. In this paper a linearly parameterized neural network is used to approximate the uncertainties of the AUV's dynamic, and the basis function vector of network is constructed according to th AUV's physical properties. A sliding mode control scheme is introduced to attenuate the effect of the neural network's reconstruction errors and the disturbances in AUV's dynamics. Using Lyapunov theory, the stability of the presented control system is guaranteed as well as the uniformly boundedness of tracking errors and neural network's weights estimation errors. Finally, numerical simulations for motion control of an AUV are performed to illustrate the effectiveness of the proposed techniques.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.4
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• pp.243-250
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• 2003

In this paper, the speed controller of permanent-magnet synchronous motor (PMSM) using the RBF neural (NN) disturbance observer is proposed. The suggested controller is designed using the input-output feedback linearization technique for the nominal model of PMSM and incorporates the RBF NN disturbance observer to compensate for the system uncertainties. Because the RBF NN disturbance observer which estimates the variation of a system parameter and a load torque is employed, the proposed algorithm is robust against the uncertainties of the system. Finally, the computer simulation is carried out to verify the effectiveness of the proposed method.