• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.5
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• pp.8-12
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• 2008

In this paper, the robust state observer for nonlinear systems with unknown disturbance is proposed. The proposed method has an advantage in that it can reduce the effect of disturbance on estimation error of observer up to a specified level. Therefore, our design a roach can deal with a larger class of uncertain nonlinear system than the existing methods. The sufficient conditions on the existence of robust observer are characterized by well grown linear matrix inequality. Finally, an illustrative example is given to verify the proposed design scheme.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.2
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• pp.82-88
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• 2005

A compensation scheme for a class of linearly parameterized systems is presented. The compensator consists of a typical linearizing control and an adaptive observer with integration type update law, which is based on Speed Gradient (SG) algorithm.. Instead of the intermediate functions of the compensation schemes suggested by other researchers, the proposed compensator is designed with some design functions which guarantee the growth, convexity, attainability, and pseudo gradient conditions in the update law. The scheme achieves the asymptotic stability of the tracking error and the boundedness of the estimation errors. A numerical example is given to demonstrate the validity of the proposed design.

• Proceedings of the KIEE Conference
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• 2004.11b
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• pp.237-239
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• 2004

이 논문에서는 Nonlinear Observer Backstepping 출력 궤환 제어에 의한 비선형 전력계통안정화장치(NPSS)를 개발하였다. 이 기법은 출력단자의 측정만으로 매우 강한 전력계통의 고유한 특징인 강한 비선형을 처리할 수 있는 바람직한 제어기를 구성할 수가 있다. 첫 번째로 비선형 관측기를 통하여 측정할 수 없는 부분의 상태들을 대신할 수 있는 출력 궤환 관측기를 구성하는 것이다. 두 번째로 이렇게 구성된 관측기에 매번 상태를 반영할 수 있는 backstepping 제어기를 적응하는 것이다. 시뮬레이션을 통한 제안된 제어기의 효과는 비 제어시, 기존의 Lead-Lag PSS, 그리고 제안된 Nonlinear Observer backstepping 출력 궤환 제어에 기준한 비선형 PSS를 비교 검토하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.168-173
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• 2009

This paper investigates the estimation problem of vehicle velocity and propeller angular velocity on the underwater vehicle. Inspired by but different from a high-gain observer, the cascade observer features a cascade structure and adaptive observer gains. In doing so the cascade observer attempts to overcome some of the typical problems that may pose to a high-gain observer. As in the case of a high-gain observer, the cascade observer structure is simple and universal in the sense that it is independent of the system dynamics and parameters. A cascade observer is used for the estimation of velocity from measured position. In the 1st step of the observer, the output is estimated, and the 1st order derivative of measured output is estimated via the 2nd step of the observer. Also, nth order derivative of the output is estimated in the (n+1)th step of the observer. It is shown that the proposed observer guarantees globally asymptotical stability. By simulation results, the proposed observer scheme for the estimations of vehicle velocity and propeller angular velocity shows better performance than the scheme based on the existing observer.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.10 s.253
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• pp.1249-1254
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• 2006

Existing adaptive observers may cause the parameter drifts due to disturbances even if state estimation errors remain small. To avoid the drift phenomena in the presence of bounded disturbances, several robust adaptive observers have been introduced addressing bounds in state and parameter estimates. However, it is not easy for these observers to manipulate the size of the bounds with the selection of the observer gain. In order to reduce estimation errors, this paper introduces the (equation omitted) gain minimization problem in the adaptive observer structure, which minimizes the (equation omitted) gain between disturbances and estimation errors. The stability condition of the adaptive observer is reformulated as a linear matrix inequality, and the observer gain is optimally chosen by solving the convex optimization problem. The estimation performance is demonstrated through a numerical example.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.1
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• pp.1-8
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• 2003

In this paper, an observer-based adaptive controller is proposed to control the longitudinal motion of vehicles. The standard gradient method is used to estimate the vehicle parameters, mass, time constant, etc. The inter-vehicle spacing and its derivatives are estimated by using the sliding mode cascade observer introduced in this paper. It is shown that the proposed adaptive controller is uniformly ultimately bounded. It is also shown that the errors of the relative distance, the relative velocity and the relative acceleration asymptotically converge to zero. The simulation results are presented to investigate the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.297-300
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• 2001

본 논문에서는 로봇 매니퓰레이터의 제어에 사용할 수 있는 신경망 외란 관측기를 제안하도록 한다. 제안한 신경망 외란 관측기는 다층신경 망의 구조로 신경망 외란관측기의 오차와 제어 오차가 충분히 작은 콤팩트 집합에 절대 상시 유계된다. 본 논문에서 제안하는 신경망 외란 관측기는 기존의 적응 제어기의 단점을 해결한 방식으로 복잡한 회귀 모델을 필요로 하지 않는다. 끝으로 제안한 방식을 3관절 로봇에 적용하여 그 타당성을 확인한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.5 no.3
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• pp.266-272
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• 2004

In this paper, an observer-based adaptive controller is proposed to control the longitudinal motion of vehicles. The standard gradient method will be used to estimate the vehicle parameters such as mass, time constant, etc. The nonlinear model between the driving force and the vehicle acceleration will be chosen to design the state observer for the vehicle velocity and acceleration. It will be shown that the proposed observer is exponentially stable, and that the adaptive controller proposed in this paper is stable by the Lyapunov function candidate. It will be proved that the errors of the relative distance, velocity and acceleration converge to zero asymptotically fast, and that the overall system is also asymptotically stable. The simulation results are presented to investigate the effectiveness of the proposed method.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.1
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• pp.50-58
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• 2009

For precise tracking control of a servo system with nonlinear friction, a robust friction compensation scheme is presented in this paper. The nonlinear friction is difficult to identify the friction parameters exactly through experiments. Friction parameters can be also varied according to contact conditions such as the variation of temperature and lubrication. Thus, in order to overcome these problems and obtain the desired position tracking performance, a robust adaptive back-stepping control scheme with a dual friction observer is developed. In addition, to estimate lumped friction uncertainty due to modeling errors, a DEKF recurrent neural network and adaptive reconstructed error estimator are also developed. The feasibility of the proposed control scheme is verified through the experiment fur a ball-screw system.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.3
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• pp.228-235
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• 2010

This paper deals with an autonomous flight controller design problem for a tilt-rotor aircraft in rotary-wing mode. The inner-loop algorithm is designed using the output-based approximate feedback linearization. The model error originated from the feedback linearization is cancelled within allowable tolerance by using single-hidden-layer neural network. According to Lyapunov direct stability theory, the adaptive update law is derived to run the neural network on-line, which is based on the linear observer dynamics. Moreover, the outer-loop algorithm is designed to track the trajectory generated from way-point guidance. Especially, heading and flight-path angle line-of-sight guidance are applied to the outer-loop to improve accuracy of the landing tracking performance. The 6-DOF nonlinear simulation shows that the overall performance of the flight control algorithm is satisfactory even though the collective input response shows instantaneous actuator saturation for a short time due to the lack of the neural network and the saturation protection logic in that loop.