• 한국조명전기설비학회지:조명전기설비
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• 제5권1호
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• pp.57-64
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• 1991

본 연구는 공장자동화에 이용되는 산업용 로봇의 분산 적응 제어기법을 고찰한 것이다. 이 제어기법은 메니폴레이터가 원하는 경로를 따라 진행할 때 부하의 변동에 신속히 대응할 수 있도록 함으로서 실시간 제어가 가능하게 하는 것이다. 제어방식은 전체 시스템을 규모가 작은 여러개의 부시스템으로 분리하고, 각 부시스템을 공칭제어기와 적응제어기로 제어하는 것이다. 이 제안한 기법을 컴퓨토 시뮬레이션한 결과 매니풀레이터 동력학이 비선형이며 부하변동이 있을 때에도 원하는 궤적에 양호하게 추종함을 알 수 있었다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2760-2762
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• 2000

The objective of this paper is to design a force feedback controller for bilateral control of a master-slave manipulator system. In a bilateral control system. the motion of the master device is followed by the save one. while the force applied to the slave is reflected on the master. In this paper, a proposed controller applied to the system. Adding a switching control term to control input. robustness is improved. Also the knowledge of the system dynamics is not needed. The computer simulation results show the performance of the proposed controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 추계학술대회 논문집 학회본부 B
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• pp.651-653
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• 1998

This thesis propose the prefilter type control strategies using modified chaotic neural networks #or the trajectory control of robotic manipulator. Since the structure of chaotic neural networks and neurons, chaotic neural networks can show the robust characteristics for controlling highly nonlinear dynamics like robotic manipulators. For its application, the trajectory controller of the three-axis PUMA robot is designed by CNN. The CNN controller acts as the compensator of the PD controller. Simulation results show that learning error decrease drastically via on- line learning and the performance is excellent. The CNN controller have much better controllability and shorter calculation time compared to the RNN controller. Another advantage of the proposed controller could be attached to conventional robot controller without hardware changes.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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• pp.445-450
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• 1998

In this paper, a robust nonlinear prediction-type controller (RNPC) is developed for the continuous time nonlinear system whose control objective is composed of system output and its desired value. The basic control law of RNPC is derived such that the future response of the system is first predicted by appropriate functional expansions and the control law minimizing the difference between the predicted and desired responses is then calculated. RNPC which involves two controls, i.e., the auxiliary and robust controls into the basic control, shows the stable closed loop dynamics of nonlinear system of any relative degree and provides the robustness to the nonlinear system with parameter/modeling uncertainty. Simulation tests for the position control of a two-link rigid body manipulator confirm the performance improvement and the robustness of RNPC.

• 대한전자공학회논문지
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• 제26권4호
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• pp.45-52
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• 1989

로보트는 일반적으로 성능에 비해 링크 및 구동기가 심히 과도설계(overdesign) 되어 있다. 로보트의 링크를 평형화(balancing) 시키면 역학이 간단해 질 뿐 아니라 지배적으로 큰 중력항을 제거시킬 수 있다는 점이 최근에 보여졌다. 본 연구에서는 평형화에 의한 중력항의 제거가 과도설계를 피하는 한 방법으로 보고 페이드로에 따라 변하는 평형조건을 적극적으로 만족시켜주는 자동평형장치(auto-balancing mechanism)을 제안하고 그 성능을 컴퓨터 시뮬레이션으로 예시하였다.

• 전자공학회논문지B
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• 제29B권10호
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• pp.37-45
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• 1992

For a precise manipulator control in the presence of environmental uncertainties, it has long been recognized that the robot should be controlled in a task-referenced space. In this respect, an effective visual servo control system for robot manipulators based on neural networks is proposed. In the proposed control system, a Backpropagation neural network is used first to learn the mapping relationship between the robot's joint space and the video image space. However, in the real control loop, this network is not used in itself, but its first and second derivatives are used to generate servo commands for the robot. Second, and Adaline neural network is used to identify the approximately linear dynamics of the robot and also to generate the proper joint torque commands. Computer simulation has been performed demonstrating the proposed method's superior performance. Futrhermore, the proposed scheme can be effectively utilized in a robot skill acquisition system where the robot can be taught by watching a human behavioral task.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1410-1413
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• 1993

This paper presents the application of fuzzy control to three links of a Rhino robot and compares its performance to traditional PD control. The dynamics of motion of robot links are governed by nonlinear differential equations. The fuzzy controller, being an adaptive technique, gives better performance than the traditional linear PD controller over a typical operational range. The fuzzy controller reaches the desired position with no overshoot, which is unlikely with the PD controller.

• KIEE International Transaction on Systems and Control
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• 제12D권1호
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• pp.43-50
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• 2002

In the recent years neural networks have fulfilled the promise of providing model-free learning controllers for nonlinear systems; however, it is very difficult to guarantee the stability and robustness of neural network control systems. This paper proposes an adaptive neural network control for robot manipulators based on the radial basis function netwo.k (RBFN). The RBFN is a branch of the neural networks and is mathematically tractable. So we adopt the RBFN to approximate nonlinear robot dynamics. The RBFN generates control input signals based on the Lyapunov stability that is often used in the conventional control schemes. The saturation function is also chosen as an auxiliary controller to guarantee the stability and robustness of the control system under the external disturbances and modeling uncertainties.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2380-2382
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• 2001

The objective of this paper is to design a force feedback controller for bilateral control of a master-slave manipulator system using fuzzy sliding mode control. In a bilateral control system the motion of the master device is followed by slave the one. While the force applied to the slave is reflected on the master. In this paper, a proposed controller applied to the system. Adding a switching control term to the input, robustness is improved. Also the knowledge of the system dynamics is not needed. The computer simulation results show the performance of the proposed fuzzy sliding mode controller.

• 대한전기학회논문지
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• 제38권11호
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• pp.941-949
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• 1989

This two-part paper presents a control method that allows for stable interaction of a robot manipulator with environment. In part 1, we focus on the input output relationships (unstructured modeling) of the robot and environment dynamics. This analysis leads to a general condition for stability of the robot and environment taken as a whole. This stability condition, for stable maneuver, prescribes a finite sensitivity for robot and environment where sensitivity of the robot (or the environment) is defined as a mapping forces into displacement. According to this stability condition, smaller sensitivity either in robot or in environment leads to narrower stability range. In the limit, when both systems have zero sensitivity, stability cannot be guaranteed. These models do not have any particular structure, yet they can model a wide variety of industrial and research robot manipulators and environment dynamic behavior. Although this approach of modeling may not lead to and design procedure, it will allow us to understand the fundamental issues in stability when a robot interacts with an environment.