• Journal of Electrical Engineering and information Science
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• v.2 no.3
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• pp.88-94
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• 1997

This paper presents the sliding mode observer-model following (SMO-MF) power system stabilizer(PSS) for unmeasurable state variables. This SMO-MF PSS is obtained by combining the sliding mode-model following (SM-MF) including closed-loop feedback(CLF) with the full-order observer(FOO). The control input of the proposed MO-MF PSS is derived by Lyapunov's second method to determine a control input that keeps the system stable for unmeasurable plant state variables. Simulation results show that the proposed SMO-MF PSS including CLF is able to reduce the low frequency oscillation and to achieve asymptotic tracking error between the reference mode state and the estimated plant state at different initial conditions.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.564-570
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• 2003

This paper presents an adaptive fuzzy control scheme for nonlinear helicopter system which has uncertainty or unknown variations in parameters. The proposed adaptive fuzzy controller is a model reference adaptive controller. The parameters of fuzzy controller are adjusted so that the plant output tracks the reference model output. It is shown that the adaptive law guarantees the stability of the closed-loop system by using Lyapunov function. Several experiments with a small model helicopter having parameter variations are performed to show the usefulness of the proposed adaptive fuzzy controller.

• Journal of National Security and Military Science
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• s.1
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• pp.311-343
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• 2003

In this paper, we propose a generalized disturbance torque suppression control scheme of servo motors for missile fin actuators. Our controller consists of both a model based feed-forward controller and a stabilizing feedback controller. The feed-forward controller is designed such that the output of nominal plant tracks perfectly the reference position command with a desired dynamic characteristics. The feedback controller stabilizes the overall closed loop system. Furthermore, the feedback controller contains a free function that can be chosen arbitrary. The free function can be designed so as to achieve both the suppression of disturbances and the robustness to model uncertainties. In order to illuminate the superior performance of our control scheme to the conventional ones, we present some simulation results.

• Proceedings of the KIPE Conference
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• 2008.06a
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• pp.247-249
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• 2008

There are many types of grid-connected inverter controllers; Synchronous Reference Frame (SRF)-based controller is the most popular methods. SRF-based controller is capable for reducing both of zero-steady state error and phase delay. However, SRF-based controller has a complex algorithm to apply in real application such as digital processor. Resonant controller is also reduced zero-steady state error, but its transfer function has a high order. In this paper, a simple proportional control is applied for grid connected inverter with LCL filter. LCL filter is a third order system. Applying a simple proportional controller is not increased the order of closed loop transfer function. By this technique, the single phase model is easily obtained. To reduce steady state error, proportional gain is set as high as possible, but it may produce instability. To compromise between a minimum steady state error and stability, the single phase model is evaluate through Root Locus and Bode diagram. PSIM simulation is used to verify the analysis.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.63-64
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• 2007

In this paper, an adaptive passivity based excitation and governor control scheme is proposed to enhance the transient stability of a single machine infinite bus power system with parametric uncertainties. We employ a state model where the frequency, the difference between active and mechanical power, and the difference between the squared terminal voltage and its reference are regarded as state variables. Using this state model, the proposed controller is obtained in two steps; firstly, a simple direct adaptive passivation controller is designed for the power system with parametric uncertainties; then a linear PI controller is applied to guarantee the stability of the closed loop system.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.3
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• pp.88-97
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• 2013

This paper presents a robust air-to-fuel ratio (AFR) control algorithm for managing exhaust gas recirculation (EGR) systems. In order to handle production tolerance, deterioration and parameter-varying characteristics of the EGR system, quantitative feedback theory (QFT) is applied for designing the robust AFR control algorithm. A plant model of EGR system is approximated by the first order transfer function plus time-delay (FOPTD) model. EGR valve position and AFR of exhaust gas are used as input/output variables of the plant model. Through engine experiments, parameter uncertainty of the plant model is identified in a fixed engine operating point. Requirement specifications of robust stability and reference tracking performance are defined and these are fulfilled by the following steps: during loop shaping process, a PID controller is designed by using a nominal loop transmission function represented on Nichols chart. Then, the frequency response of closed-loop transfer function is used for designing a prefilter. It is validated that the proposed QFT-based AFR control algorithm successfully satisfy the requirements through experiments of various engine operating points.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.731-736
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• 1994

Experiment on a lab-scale pH process is carried out to evaluate the control performance of the neural linearizing control scheme(NLCS) using a radial basis function(RBF) network which was previously proposed by Kim and Park. NLCS was developed to overcome the difficulties of the conventional neural controllers which occur when they are applied to chemical processes. Since NLCS is applicable for the processes which are already controlled by a linear controller and of which the past operating data are enough, we first control the pH process with PI controller. Using the operating data with PI controller, the linear reference model is determined by optimization. Then, a IMC controller replaces the PI controller as a feedback controller. NLCS consists of the IMC controller and a RBF network. After the learning of the neural network is fully achieved, the dynamics of the process combined with the neural network becomes linear and close to that of the linear reference model and the control performance of the linear control improves. During the training, NLCS maintains the stability and the control performance of the closed loop system. Experimental results show that the NLCS performs better than PI controller and IMC for both the servo and the regulator problems.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.12
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• pp.1170-1176
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• 2010

This study addresses adaptive control of UAVs(Unmanned Aerial Vehicles) pitch-axis maneuver. The MRAC(Model Referenced Adaptive Control) approach is employed to accommodate uncertainties which are introduced by feedback linearization of pitch attitude control by elevator input. The model uncertainty is handled by adaptation laws which update model parameters while the UAV is under control by the feedback control law. Steady-state pitch attitude achieved by the stabilizing control law is derived to provide insight on the closed-loop behavior of the controlled system. The proposed idea is free of linearization, gain-scheduling procedures, so that one can design high maneuverability of UAVs for pitching motion in the presence of significant model uncertainty.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.177-180
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• 1996

The purpose of this paper is to design a robust adaptive control algorithm for a class of systems having continuous relay nonlinearity. This continuous relay nonlinearity can be defined as an analytic nonlinear function having unknown parameters and bounded unmodeling part. By this mathematical modeling, the whole system can be considered as a nonlinear system having unknown parameters and bounded perturbation. The control algorithm of this paper, RALC, can be constructed by robust adaptive law, feedback linearization, and indirect robust adaptive control. By this RALC, we can obtain that the output of given system can follow that of a stable reference linear model made by designer and the boundedness of all signals in closed-loop system can be maintained. Therefore, we can confirm a robust adaptive control for a class of systems having continuous relay nonlinearity.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.302-305
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• 1999

Twin-servo mechanism is used to increase the payload capacity and speed of high precision motion control system. In this paper, we propose a robust synchronizing motion control algorithm to cancel out the skew motion of twin-servo system caused by different dynamic characteristics of two driving systems and the vibration generated by high accelerating and decelerating motions. This proposed control algorithm consists of separate feedback motion control algorithm of each driving system and skew motion compensation algorithm between two systems. Robust model reference tracking controller is proposed as a separate motion controller and its disturbance attenuation property is shown. For the synchronizing motion, skew motion compensation algorithm is designed, and the stability of whole Closed loop system is proved based on passivity theory.