• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.7
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• pp.997-1004
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• 1990

In this paper, a design method of a robust adaptive regulator with feedfoward path based on the explicit criterion minimization is proposed. The convergence speed of parameter estimation is improved by using the stochastic Newton minimization method in the criterion minimization algorithm, and sensitivity derivatives are used in the regulator calculation for improving the robustness of the control system. Trh proposed adaptive regulator is applied to the stable minimum-phase and nonminimum-phase system, the results are shown that control performance and disturbance compensation ability of the regulator are improved. And the choosing method of input penalty is proposed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.411-416
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• 2010

In this paper, an integral variable structure static output feedback controller with an integral-augmented sliding surface is designed for the improved robust control of a uncertain system under unmatched system uncertainty and matched input matrix uncertainty and disturbance satisfying some conditions. To effectively remove the reaching phase problems, an output dependent integral augmented sliding surface is proposed. Its equivalent control and ideal sliding mode dynamics are obtained. The previous some limitations is overcome in this systematic design. A stabilizing control with the closed loop exponential stability is designed for all unmatched system matrix uncertainties and proved together with the existence condition of the sliding mode on S=0. To show the usefulness of the algorithm, a design example and computer simulations are presented.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.51-52
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• 2007

이 논문에서는 다중입출력 선형 시스템에 대한 외란 관측기설계 방법을 제안하고자 한다. 제안된 방법을 특이 섭동(Singular Perturbation) 이론을 사용하여 해석하며, 그 결과 Q 필터의 상수가 매우 작다면 외란 제거와 시스템 불확실성에 대한 보상을 통하여 실제 시스템을 공칭 시스템으로 근사화 시켜주는 것을 볼 수 있다.

• The KIPS Transactions:PartA
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• v.8A no.1
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• pp.56-62
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• 2001

This paper presents a neural network based variable structure control scheme for nonlinear systems. In this scheme, a set of local variable structure control laws are designed on the basis of the linear models about preselected representative points which cover the range of the system operation of interest. From the combination of the set of local variable structure control laws, neural networks infer the approximate control input in between the operating points. The neural network based variable structure control alleviates the effects of model uncertainties, which cannot be compensated by the control techniques using feedback linearization. It also relaxes the discontinuity in the system’s behavior that appears when the control schemes based on the family of the linear models are applied to nonlinear systems. Simulation results of a ball and beam system, to which feedback linearization cannot be applied, demonstrate the feasibility of the proposed method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.7
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• pp.385-392
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• 2003

A nonlinear speed controller for a separately excited DC motor (SEDCM) based on a newly developed adaptive backstepping approach is presented. To compensate parameter uncertainties and load torque disturbance, a nonlinear adaptive backstepping control law and adaptive law are derived systematically step by step through virtual control input and suitable Lyapunov function. Also, speed observer without using costly speed sensor is presented. Simulation results show that the proposed controller can observe real speed and track the reference speed signal generated by a reference model.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1203-1205
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• 1998

Fault Detection of power system must be rapid and precise over input signal without relation to any disturbance. But, it is difficult to detect current unbalance, over voltage, and underfrequency for digital relay comparison of fault perfectly. In this paper, we measure each phase current and infer type of fault using fuzzy pattern classification.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.855-858
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• 1995

A neural network has very simple construction (input, output and connection weight) and then it can be robusted against some disturbance. In this paper, we proposed a neuro-controller using a Multi-Layered neural network which is combined with PD controller. The proposed neuro-controller is learned by backpropagation learning rule with momentum and neuro-controller adjusts connection weight in neural network to make approximate dynamic model of DC Servo motor. Computer Simulation results show that the proposed neuro-controller's performance is better than that of origianl PD controller.

• Advances in robotics research
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• v.2 no.1
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• pp.99-112
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• 2018

The main contribution of this work is the design of a field programmable gate array (FPGA) based ARX-Laguerre proportional-integral observation (PIO) system for fault detection and identification (FDI) in a multi-input, multi-output (MIMO) nonlinear uncertain dynamical robot manipulators. An ARX-Laguerre method was used in this study to dynamic modeling the robot manipulator in the presence of uncertainty and disturbance. To address the challenges of robustness, fault detection, isolation, and estimation the proposed FPGA-based PI observer was applied to the ARX-Laguerre robot model. The effectiveness and accuracy of FPGA based ARX-Laguerre PIO was tested by first three degrees of the freedom PUMA robot manipulator, yielding 6.3%, 10.73%, and 4.23%, average performance improvement for three types of faults (e.g., actuator fault, sensor faults, and composite fault), respectively.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.2
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• pp.178-186
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• 1998

In this paper, a robust.adaptive learning control scheme is presented for precise trajectory tracking of rigid mobile robots. In the proposed controller, a set of desired trajectories is defined and used in constructing the control input and learning rules which constitute the main part of the proposed controller. Stable operating characteristics such as precise trajectory tracking, parameter estimation, disturbance suppression, etc., are shown thorugh experiments and computer simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.7
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• pp.529-536
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• 2000

A robust controller to attenuate the various disturbances of tandem cold mills (TCM) is synthesized by measurement-feedback $H_{\infty}$ control techniques which can reflect the input direction of disturbances and the knowledge of entry thickness variation (disturbance) is synthesized by discrete-time measurement-feedback $H_{\infty}$ control theory. It is demonstrated that the $H_{\infty}$ preview control gain can be easily obtained by the seperation principle of the control and estimation problems in $H_{\infty}$ control. Finally the effectiveness of the proposed control method for TCM is evaluated by the computer simulation and compared to the other control methods which have been previously studied.