• 한국생산제조학회지
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• 제19권1호
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• pp.121-127
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• 2010

We consider the robust trajectory tracking control problem for a skid steering mobile robots. A dynamic model is derived accounting for the effects of wheel skidding. The control design utilizes the dynamic feedback linearization techniques, so as to obtain a predictable behavior for the instantaneous center of rotation thus preventing excessive skidding. The additive controller using the sliding mode type is then robustified against the unmodelled dynamics and parameter uncertainty. Simulation results show the good performances under excessively uncorrected estimations of the longitudinal forces and the lateral resistive forces caused by the skidding of the wheels in tracking trajectories.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 D
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• pp.2191-2193
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• 2004

The Sliding Mode Control is more robust and give the better performance than the $H_{\infty}$ control if the matching condition is satisfied. So in this paper, a controller which can have the advantages of $H_{\infty}$ control and the SMC is proposed to add the robustness of the SMC to the $H_{\infty}$ controller. The dynamic of proposed sliding surface is the same dynamic as the system controlled by $H_{\infty}$ controller without the uncertainties which satisfy the matching condition.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권12호
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• pp.518-525
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• 2006

This paper presents the adaptive robust control method for the flight control systems with model uncertainties. The proposed control system can be composed simply by a combination of the adaptive dynamic surface control (DSC) technique and the self recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides us with the ability to overcome the 'explosion of complexity' problem of the backstepping controller. The SRWNNs are used to observe the arbitrary model uncertainties of flight systems, and all their weights are trained on-line. From the Lyapunov stability analysis, their adaptation laws are induced and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a high performance aircraft (F-16) are utilized to validate the good tracking performance and robustness of the proposed control system.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 1999년도 학술대회논문집-국제 전기방전 및 플라즈마 심포지엄 Proceedings of 1999 KIIEE Annual Conference-International Symposium of Electrical Discharge and Plasma
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• pp.121-126
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• 1999

In this paper, to bring under robust and accurate control of auto-equipment systems which disturbance, parameter alteration of system, uncertainty and so forth exist, neural network controller called dynamic neural processor(DNP) is designed. In order to perform a elaborate task like as assembly, manufacturing and so forth of components, tracking control on the trajectory of power coming in contact with a target as well as tracking control on the movement course trajectory of end-effector is indispensable. Also, the learning architecture to compute inverse kinematic coordinates transformations in the manipulator of auto-equipment systems is developed and the example that DNP can be used is explained. The architecture and learning algorithm of the proposed dynamic neural network, the DNP, are described and computer simulations are provided to demonstrate the effectiveness of the proposed learning method using the DNP.

• 한국조명전기설비학회지:조명전기설비
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• 제13권2호
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• pp.187-187
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• 1999

in order to perform a elaborate task like as assembly, manufacturing and so forth of components, tracking control on the trajectory of power coming in contact with a target as well as tracking control on the movement course trajectory of end-effector is indispensable. In this paper, to bring under robust and accurate control of auto-equipment systems which disturbance, parameter alteration of system, uncertainty and so forth exist, neural network controller called dynamic neural processor(DNP) is designed. Also, the learning architecture to compute inverse kinematic coordinates transformations in the manipulator of auto-equipment system is developed and the example that DNP can be used is explained. The architecture and learning algorithm of the proposed dynamic neural network, the DNP, are described and computer simulation are provided to demonstrate the effectiveness of the proposed learning method using the DNP.

• 대한기계학회논문집
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• 제16권6호
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• pp.1141-1149
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• 1992

본 연구에서는 Davison이 제안한 견실 제어이론을 응용하여, 자기베어링에 의 해서 지지되는 로터계를 위한 중앙 집중식 서보제어기를 설계하였다. 삼각함수 형태 의 외란과 지령치에 대한 완벽한 영향회피와 추적을 위하여, 일반적 서보보상기(serv- ocompensator)를 MIMO 제어기에 내장하였다. 또한, 상기 제어기의 일부분(subset)으 로서, 중앙집중식 PID 제어기를 제안하였다. 제2장에 자기베어링에 의해 지지되는 강체 로터계의 동적 모델을 요약하였으며, 제3장에서 제어기법의 구축을 설명하고, 두 가지 형태의 제어기에 대한 성능 비교와 견실성의 한계를 보여주는 시뮬레이션 결과를 제 4장에 제시하였다.

• 한국정밀공학회지
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• 제17권6호
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• pp.135-142
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• 2000

In this paper, 3-dimensional numerical model of an escalator is developed to study the vibration characteristics. This proposed model is able to consider the elastic deformation of the frame during transient dynamic analysis. Deformation modes which are used to calculate the elastic deformation are selected from the FE model analysis. Because low frequency vibration is very important to the ride quality of fore/aft direction, low frequency deformation modes of the frame below 20Hz are considered. To show validity of this dynamics model, longitudinal acceleration of a step is compared with test data in frequency domain. Then robust design technique is applied to determine important design factors and improve ride quality with small number of experiments.

• 한국지능시스템학회논문지
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• 제14권5호
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• pp.557-562
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• 2004

이 연구는 T-S 퍼지시스템으로 모델 되는 비선형 시스템의 견실한 혼합 ${H_2}/{H_{\infty}}$ 필터 설계문제를 취급한다. 플랜트에 포함된 다양한 종류의 불확실성을 취급하기 위하여 적분 2차 제약조건을 사용하였다. 필터 설계문제의 해가 존재할 충분조건을 볼록 최적화 기법을 사용하여 효과적으로 풀 수 있는 선형 행렬 부등식의 형태로 제시한다. 제시된 방법을 예시하기 위해서 수치 예를 보여준다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.231-234
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• 1996

This paper considers the output regulation problems on uncertain systems. Using NR-estimator(on-line), a family of equilibrium points for the uncertain system is computed. The state variables of the closed loop system track the average value of the obtained equilibrium manifold by dynamic state feedback control.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.384-384
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• 2000

In adaptive neuro-control, neural networks are used to approximate the unknown plant nonlinearities. Until now, most of the papers in the field of controller design fur nonlinear system using neural networks considers the affine system with fixed number of neurons. This paper considers nonaffne nonlinear systems and dynamic variation of the number of neurons. Control laws and adaptive laws for weights are established so that the whole system is stable in the sense of Lyapunov.