• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.463-468
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• 2002

In this paper, we proposed a recurrent time delayed neural network controller which compensate a output of neural network controller. Even if learn by neural network controller, it can occur an bad results from disturbance or load variations. So in order to adjust above case, we used the fuzzy compensator to get an expected results. And the weight of main neural network can be changed with the result of learning a inverse model neural network of plant, so a expected dynamic characteristics of plant can be got. As the results of simulation through the second order plant, we confirmed that the proposed recurrent time delayed neural network controller get a good response compare with a time delayed neural network controller.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.3
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• pp.603-610
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• 2000

In this paper, the direct and indirect adaptive controller are combined based on the Lyapunov synthesis approach. The Proposed controller is constructed from RBF Neural Network and weighting parameters are adjusted on-line according to some adaptation law. In this scheme, fuzzy IF-THEN rules are used to decide the combined weighting factor. In the results, proposed controller has the main advantages of both the direct adaptive controller and the indirect adaptive controller. The effectiveness of the proposed control scheme is demonstrated through simulation results of control for one-link rigid robotics manipulator.

• International Journal of Control, Automation, and Systems
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• v.3 no.spc2
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• pp.346-354
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• 2005

This paper presents a new approach to Thyristor Controlled Phase Shifter (TCPS) control. In this paper we have proposed a nonlinear coordinated generator excitation and TCPS controller to enhance the transient stability of a power system. The proposed controller is able to control three main parameters affecting a.c. power transmission: namely excitation voltage, phase angle and reactance in a coordinated manner. The TCPS is located at the midpoint of the transmission line. A nonlinear feedback control law is proposed to linearize and decouple the power system. The design of the proposed controller is based on the local measurements only. Simulation results have been shown to demonstrate the effectiveness of the proposed controller for the enhancement of transient stability of the power system under a large sudden fault.

• Journal of Information Technology Applications and Management
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• v.16 no.4
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• pp.263-283
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• 2009

Although some CASE tools supported conceptual data design, they required for users too much preliminary knowledge to learn how to use and handle them. In addition, in a number of studies on conceptual data design with natural language, they claimed passive participation for users with conforming to messages predefined by CASE tools. As an alternative to these traditional CASE tools, we proposed an ERD formulator for automated data design tool, called ERDF, so that even ordinary users, not necessarily data modeler, are capable of formulating ERD on business requirements by use of ERDF. We, first of all, introduced NSM as the standard methodology. We also designed the structure of ERDF including main controller, input controller, operation controller, regulation controller, schema controller, and output controller. We then defined conceptual domains and basic operations to lay down schema operations as well as sentence rules to handle input sentence in natural language. To get an ERD that is faithful to business requirements, we laid out supplementary design for dialogue and confirmation of soundness and completion.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.791-793
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• 1999

In this paper, neural network-supervisory control method is proposed to minimize the effect of system uncertainty by load change and disturbance in the PID control system. In the proposed method, PID controller performs main control action by performing control within constraint error. And neural network-supervisory controller performs control action when error reaches the boundary of constraint error. Combining neural network-supervisory controller to guarantee the stability into PID control system, the resulting PID control system is expected to show better performance in the system with load change and disturbance. Simulation applying PID controller and neural network-supervisory controller showed excellence of proposed method.

• Journal of Mechanical Science and Technology
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• v.19 no.2
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• pp.618-624
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• 2005

We present a new state-space approach to construct a dynamic output feedback controller which stabilizes a class of linear time invariant systems. All the states of the given system are not measurable and only the output is used to design the stabilizing control law. In the design scheme, however, we first assume that the given system can be stabilized by a feedback law composed of the output and its derivatives of a certain order. Beginning with this assumption, we systematically construct a dynamic system which removes the need of the derivatives. The main advantage of the proposed controller is regarding the controller order, which may be smaller than that of conventional output feedback controller. Using a simple numerical example, it is shown that the order of the proposed controller is indeed smaller than that of reduced-order observer based output feedback controller.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.5-8
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• 1999

The main stream of researches on the mobile robot is planning motions of the mobile robot under nonholonomic constraints while only considering kinematic model of a mobile robot. These researches, however, assume that there is some kind of dynamic controller which can produce perfectly the same velocity that is necessary for the kinematic controller. Moreover, there are little results about the problem of integrating the nonholonomic kinematic controller and the dynamic controller for a mobile robot. Also the literature on the robustness of the controller in the presence of uncertainties or external disturbances in the dynamical model of a mobile robot is very few. Thus, in this paper, the robust adaptive controller which can achieve velocity tracking while considering not only kinematic model but also dynamic model of the mobile robot is proposed. The stability of the dynamic system will be shown through the Lyapunov method.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.376-379
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• 1993

GoldStar Industrial Systems Co. R & D center successfully developed the inverter for welding under the support of GoldStar Electric Machinery Co. Now, we are currently working on the mechanical part of articulated robot and a high-performance general purpose motion controller. By combining the above three items, we will be able to constitute the complete welding robot system on our own. In this article. the welding robot system currently under developing is introduced. The main focus will be placed on the development of general purpose motion controller with welding control module. Therefore, the architecture of welding robot controller where the general purpose motion controller is combined with the welding controller module will be explained. Here, the software system will be explained with regard to the hardware system.

• Journal of Power Electronics
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• v.11 no.4
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• pp.393-400
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• 2011

The main objective of this paper is to control the speed of Nonlinear Hybrid Electric Vehicle (HEV) by controlling the throttle position. Various control techniques such as well known Proportional-Integral-Derivative (PID) controller in conjunction with state feedback controller (SFC) such as Pole Placement Technique (PPT), Observer Based Controller (OBC) and Linear Quadratic Regulator (LQR) Controller are designed. Some Intelligent control techniques e.g. fuzzy logic PD, Fuzzy logic PI along with Adaptive Controller such as Self Organizing Controller (SOC) is also designed. The design objective in this research paper is to provide smooth throttle movement, zero steady-state speed error, and to maintain a Selected Vehicle (SV) speed. A comparative study is carried out in order to identify the superiority of optimal control technique so as to get improved fuel economy, reduced pollution, improved driving safety and reduced manufacturing costs.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.2
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• pp.97-106
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• 1999

This paper proposes the structure and the algorithm of the Fuzzy-Neural Controller(FNNC) which is able to adapt itself to unknown plant and the change of circumstances at the Fuzzy Logic Controller(FLC) with the Neural Network. This Learning Fuzzy Logic Controller is made up of Fuzzy Logic controller in charge of a main role and Neural Network of an adaptation in variable circumstances. This construct optimal fuzzy controller applied to the 2-area load frequency control of power system, and then it would examine fitness about parameter variation of plant or variation of circumstances. And it proposes the optimal Scale factor method wsint three preformance functions( E, , U) of system dynamics of load frequency control with error back-propagation learning algorithm. Applying the controller to the model of load frequency control, it is shown that the FNNC method has better rapidity for load disturbance, reduces load frequency maximum deviation and tie line power flow deviation and minimizes reaching and settling time compared to the Optimal Fuzzy Logic Controller(OFLC) and the Optimal Control for optimzation of performance index in past control techniques.