• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.4 no.3
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• pp.41-49
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• 1990

본 연구는 산업현장에서 요구되고 있는 공장자동화의 한 분야로 다관절이 로보트의 비집중 제어기버에 관해 고찰한 것이다. 제안한 기법은 매니퓰레이터가 원하는 궤적에 정확히 진행할 수 있도록 각 링크별로 차원이 낮은 부시스템으로 모델링하여 분산제어를 실시 함으로서 실시간 제어를 용이하게 할 수 있는 방식이다. 각 부시스템의 제어기는 관절별로 제어하는 궤환 제어기, 전향 제어기 및 보호신호기 등으로 구성되고, 이들 제어기의 이득을 조정함으로서 매니퓰레이터를 제어하는 것이다. 또한 제안한 비집중 적응 제어기를 2-링크 매니퓰레이터에 시뮬에이션한 결과 강하게 결합된 동력학과 부하의 변화에도 불구하고 제안한 기법이 원하는 궤적에 양호하게 진행함을 알 수 있었다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.5
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• pp.634-642
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• 1993

In this paper a new trajectory control method is proposed for a robot manipulator using a time delay neural network(TDNN) as a feedforward controller with an algorithm to learn inverse dynamics of the manipulator. The TDNN structure has so favorable characteristics that neurons can extract more dynamic information from both present and past input signals and perform more efficient learning. The TDNN neural network receives two normalized inputs, one of which is the reference trajectory signal and the other of which is the error signals from the PD controller. It is proved that the normalized inputs to the TDNN neural network can enhance the learning efficiency of the neural network. The proposed scheme was investigated for the planar robot manipulator with two joints by computer simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.1
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• pp.61-66
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• 1997

로보트 매니퓰레이터가 일정한 접촉 힘을 유지하며 공작물의 표면을 따라가게 하는 작업은 많은 자동화 생산공정에서 유용하게 이용될 수 있다. 일반적인 위치제어용 산업용 로보트를 이러한 공정에 사용하기 위해서는 접촉힘이 계측되어 로보트의 제어에 이용되어야만 한다. 이 연구는 accommodation force control 방식으로 산업용 로보트를 제어하여 edge-following에 응용하도록하며, 접촉 힘의 계측에는 wrist force sensor를 사용한다. 이 시스템의 궤도추적속도와 force regulation 등이 예견제어에 의해 향상될 수 있다. 예견제어에 의해 설계된 전체 제어 시스템은 feedback 제어기와 feedforward 예견제어기로 구성된다. 여기서, 시스템의 안정성은 feedback 제어기에 의해서 결정되며, 예견제어기는 시스템에 미치는 외란을 통제하는 것을 주 기능으로 한다. 일반적으로 선형제어 방식을 채택한 경우와 예견제어를 이용한 edge-following을 실험을 통해 비교함으로써, 예견제어의 효용성을 확인한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.13 no.3
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• pp.260-269
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• 1988

Conventional torque and position control scheme is developed to control a robot manipulator using the knowing mass of links of load. This paper presents a torque and position control method which performs under unknown mass of links or load. This method takes full advantage of known parameters and servo error in joint coordinate space. We illutrate the theory with some simulations and show that the results is effective.

• Journal of the Korean Institute of Intelligent Systems
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• v.4 no.3
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• pp.14-22
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• 1994

• Proceedings of the KIEE Conference
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• 1987.07a
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• pp.238-240
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• 1987

• Proceedings of the KIEE Conference
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• 1987.07a
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• pp.249-251
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• 1987

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.10
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• pp.53-59
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• 1990

In the control of robot manipulators, this paper presents a design of a new variable structure model following controller(VSMFC) using computed torque method (CTM). A sufficient condition for the existence of a sliding mode is derived by Lyapunov function. The reference model is a double integrators and the acceleration input consists of a proportional-derivative controller for the purpose of the stabilization of system and the desired performance. The proposed control scheme which consists of upper bounded and estimated value of each term of the manipulator of matrix inversion. Therefore the simulation results show that this controller is improved to the convergence of desired trajectories.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.1
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• pp.52-64
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• 1995

Robot manipulator is a highly nonlinear-time varying system. Therefore, a lot of control theory has been applied to the system. Robot manipulator has two types of control; one is path planning, another is path tracking. In this paper, we select the path tracking, and for this purpose, propose the intelligent control¬ler which is combined with fuzzy logic and neural network. The fuzzy logic provides an inference morphorlogy that enables approximate human reasoning to apply to knowledge-based systems, and also provides a mathematical strength to capture the uncertainties associated with human cognitive processes like thinking and reasoning. Based on this fuzzy logic, the fuzzy logic controller(FLC) provides a means of converhng a linguistic control strategy based on expert knowledge into automahc control strategy. But the construction of rule-base for a nonlinear hme-varying system such as robot, becomes much more com¬plicated because of model uncertainty and parameter variations. To cope with these problems, a auto-tuning method of the fuzzy rule-base is required. In this paper, the GA-based Fuzzy-Neural control system combining Fuzzy-Neural control theory with the genetic algorithm(GA), that is known to be very effective in the optimization problem, will be proposed. The effectiveness of the proposed control system will be demonstrated by computer simulations using a two degree of freedom robot manipulator.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.4
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• pp.45-52
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• 1989

The size of links and actuators of robots tends to be overdesigned compared with their performance. It is recently reported that the balancing of a robot links simplifies its dynamics and eliminates the gravity terms. As a way of avoiding overdesign problem, an auto-balancing mechanism is proposed, which enables to satisfy varying balancing condition according to payloads. The performance of this auto-balancing mechanism is illustrated by the computer simulation evidences.