• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.474-478
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• 1998

This paper proposes a method which can resolve the problem of exisiting fuzzy PI controller using optimal scaling gains obtained by genetic algorithm. The new method adapt a fuzzy logic controller as a high level controller to perform scaling gain algorithm between two pre-determined sets.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.465-468
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• 1998

In this paper, merits of both fuzzy and PID controllers are combined. The combined controller is designed such that the tuning of the PID controller is achieved by the basic fuzzy controller via its rule base. The proposed scheme avoids the tuning of PID parameters which is always a time consuming task, difficult to carry out and often poorly done. Computer simulations are made to demonstrate the satisfactory tracking performance of the combined fuzzy-PID controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.121-124
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• 1997

In this paper, applications of self-recurrent neural networks based of adaptive controller to position control of flexible robot link are considered. The self-recurrent neural networks can be used to approximate any continuous function to any desired degree of accuracy and the weights are updated by feedback-error learning algorithm. Therefore, a comparative analysis was mode with linear controller through an simulation. The results are presented to illustrate the advantages and improved performance of the proposed position tracking controller over the conventional linear controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.185-188
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• 1997

In this paper, a fuzzy precompensated PI controller for inverter-type air-conditioner is presented. The presented control scheme is composed of a fuzzy logic precompensator and PI controller, in which two control schemes are serially connected. The rules of the fuzzy precompensator is designed to improve the performance by considering the nonlinear characteristics of a temperature dynamics. The experimental results show the effectiveness of the proposed controller.

• Proceedings of the KIEE Conference
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• 2000.11a
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• pp.33-35
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• 2000

We propose a intelligent controller for FACTS(Flexible AC Transmission System) device to stabilize a power system. In order to identify the nonlinear characteristics of the power system and to estimate a control signal, an artificial neural network is utilized. The control signal which is provided for FACTS device installed in the network is produced. The proposed controller is applied to Unified Power Flow Controller(UPFC) to verified the effectiveness of the proposed control system. The results show that the proposed nonlinear FACTS controller is able to enhance the transient stability of three machine nine bus power system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.172-175
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• 1996

In the paper is proposed a hierarchical self-learning fuzzy controller for balancing and position control of an circular inverted pendulum system. To stabilize the pendulum at a specified position, the hierarchical fuzzy controller consists of a supervisory controller, a self-learning fuzzy controller, and a forced disturbance generator. Simulation example shows the effectiveness of the proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.43-46
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• 2001

We, in this paper, design the Sugeno-models fuzzy controller by using the membership function modification algorithm and ANFIS, which are clustering and learning the input-output data. The membership function modification algorithm constructs the more concrete fuzzy controller by clustering the input-output data from the fuzzy inference system. ANFIS construct the Sugeno-models fuzzy controller by learning the input-output data from the above controller. We showed that the fuzzy controller designed by our method could have the stable learning and the enhanced performance.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.146.2-146
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• 2001

Recently, a need of non-industrial robot, such as service, medical, entertainment and house-keeping robot, has been increased. Therefore, the capability of robot which can do intelligent behavior like interaction with men and its environment become more prominent than the capability of executing simple repetitive task. To implement an intelligent robot which provides intelligent behavior, an effective system architecture including perception, learning, reasoning and action part is necessary. Control architectures for intelligent robot can be divided into two different classes. One is deliberative type controller which is applicate to high level intelligence like environment ...

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.8
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• pp.1224-1230
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• 2015

This paper is concerned with applicability of intelligent PID controller which is proposed by Fliss and Join recently. First, we analyze the stability regions of intelligent PID control systems when parameter α is varying, and propose a new method to determine the suitable range of α by using the roots locus. Second, the simulation study of magneto-rheological (MR) damper to the structural vibrations due to earthquakes is presented to verify the effectiveness of the intelligent PID control method.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1289-1294
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• 2003

This paper proposes a novel and efficient intelligent digital redesign technique for a Takagi-Sugeno (TS) fuzzy system. The term of intelligent digital redesign involves converting an existing analog fuzzy-model-based controller into an equivalent digital counterpart in the sense of state matching. In this paper, we suggest the discretization method based on the dual-rate sampling approximation is first proposed, and then attempt to globally match the states of the overall closed-loop TS fuzzy system with the pre-designed analog fuzzy-model-based controller and those with the digitally redesigned fuzzy-model-based controller. To show the feasibility and the effectiveness of the proposed method, a computer simulation is provided.