• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.362-362
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• 2000

The purpose of this thesis is to reduce force ripple of linear pulse motor(LPM) using neural network and to enhance precision. In order to this, we propose a new controller using a neural network to compensate disturbances. The structure includes adaptation block which learns the dynamics of the periodic disturbance and forces the interferences, caused by disturbances. The proposed controller compensates an unmodeled dynamics in the LPM. The neural network changes a current command to reduce position error and force ripple of the LPM. We compare proposed controller with PI controller. Simulation result shows that the proposed controller has better performance than a PI controller without neural network.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.3
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• pp.229-237
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• 2001

In this paper, we propose two design methods of neural networks controller for the height control of helicopter, one is the design of neural network controller having learning capability and the other is the design of more improved neural network controller. Through the simulation results, we show that the proposed controllers have controllers have enhanced control performance(rapid response, effectiveness and safety) than the typical neural networks controller in the height control of helicopter.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.463-471
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• 1999

This paper presents a vector controlled induction motor is implemented by neural networks system compared with PI controller for the speed control. The design employed the training strategy with Neural Network Controller(NNC) and Neural Network Emulator(NNE) for speed. In order to update the weights of the controller First of all Emulator updates its parameters by identifying the motor input and output next it supplies the error path to the output stage of the controller using backpropagation algorithm, As Controller produces an adequate output to the system due to neural networks learning capability Vector controlled induction motor characteristics actual motor speed with based on neural network system follows the reference speed better than that of linear PI speed controller.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.1
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• pp.85-89
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• 2004

Robust control for feedback control system is needed according to the highest precision of industrial automation. However, when a neural network feedback control system has an effect of disturbance, it is very difficult to guarantee the robustness of control system. As a compensation method solving this problem, in this paper, hybrid control method of neural network controller and PID controller is presented. A neural network controller is operated as a main controller, a PID controller is a assistant controller which operates only when some undesirable phenomena occur, e.q., when the error hit the boundary of constraint set. The robust control function of neural network-PID hybrid controller is demonstrated by speed control of Motor.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.68-74
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• 2008

In this paper, a novel neural fuzzy control method is proposed to control nonlinear systems. A standard PD-like fuzzy controller is designed and used as a main controller for the system. Then a neural network controller is added to the reference trajectories to form a neural-fuzzy control structure and used to compensate for nonlinear effects. Two neural-fuzzy control schemes based on two well-known neural network control schemes, the feedback error learning scheme and the reference compensation technique scheme as well as the standard PD-like fuzzy control are studied. Those schemes are tested to control the angle and the position of the inverted pendulum and their performances are compared.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.476-479
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• 1992

This paper presents a neural network method for tuning PlD controller of a time-varying process. Three gains of PlD controller are tuned for a certain desirable response pattern by back-propagation neural network. The neural network is trained using changes of output features vs. changes of PlD gains. But sometimes it needs longer training time and larger structure to train the correlation between the process and controller on entire region of the process. The difficulty in system identification is that the inverse function of the system can not be clearly stated. To cope with the problem, we do not train the neural network to respond correctly for the entire regions but train for only local region where the system is heading toward by training the neural network and tuning of the PlD controller. It may be trained for fine-tuning itself. Simulation results show that the adaptive PID controller using neural network trained in the local area performs remarkably for time-varying second order process.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.8
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• pp.689-696
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• 2000

In this paper we propose an eminent controller for wheeled mobile robots. This controller consists of an input-output linearization controller trying to stabilize the system and a neural network controller to compensate for uncertainties. The uncertainties are divided into two parts. First unstructured uncertainties include the elements related with system order such as friction disturbance. Second structure uncertainties are the incorrect system parameters A neural network structure of the proposed overall controller learns structural errors of the wheeled mobile robots with uncertainties and includes the neural network output. This controller learns quickly the model and has good tracking performance Simulation results show that the proposed controller is more efficient than analog controllers.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2000.11a
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• pp.159-167
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• 2000

During the operation of crane system in the container yard, it is necessary to control the crane trolley position so that the swing of the hanging container is minimized. Recently an automatic control system with high speed and rapid transportation is required. Therefore, we designed a controller to control the crane system with disturbances and weight change. In this paper, we present the neural network two degree of freedom PID controller to control the swing motion and trolley position. Then we executed the computer simulation to verify the performance of the proposed controller and compared the performance of the neural network PID controller with our proposed controller in terms of the rope swing and the precision of position control. Computer simulation results show that the proposed controller has better performances than neural network PID with disturbances.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.9
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• pp.569-575
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• 2004

This paper is proposed adaptive fuzzy-neural network(FNN) controller for high performance of induction motor drive. The design of this algorithm based on FNN controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control Performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strong high performance and robustness to parameter variation. and steady- state accuracy and transient response.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2827-2829
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• 2005

In this paper, a direct controller for nonlinear plants using a neural network is presented. The controller is composed of an approximate controller and a neural network auxiliary controller. The approximate controller gives the rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not put too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network is trained and the system has a stable performance for the inputs it has been trained for. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.