• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.253-257
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• 2004

Neural networks are works are used in the framework of sensor based tracking control of robot manipulators. They learn by practice movements the relationship between PSD(an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple back propagation networks one of which is selected according to which division(corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.450-455
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• 1989

In this paper we investigate the application to the motion control of n-link robotic manipulators of recently developed stable factorization approach to tracking and disturbance rejection. Given control scheme consists of an approximate "Computed Torque" based upon a simplified model together with additional state feedback and feedforward compensation, and then, nonlinear control gain has more useful than constant control gain to guarantee robustness to parameter uncertainty and external disturbance. At this stage, we design high gain nonlinear state feedback controller and simulate this controller at the SCARA type robot manipulator of two joint.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.436-440
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• 1992

As an approach to design the intelligent controller, this paper proposes a new FNN(Fuzzy Neural Network) control method using the hybrid combination of fuzzy logic control and neural network. The proposed FNN controller has two important capabilities, namely, adaptation and learning. These functions are performed by the following process. Firstly, identification of the parameters and estimation of the states for the unknown plant are achieved by the MNN(Model Neural Network) which is continuously trained on-line. And secondly, the learning is performed by FNN controller. The error back propagation algorithm is adopted as a learning technique. The effectiveness of the proposed method will be demonstrated by computer simulation of a two d.o.f. robot manipulator.

• Journal of the Korean Society of Industry Convergence
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• v.7 no.2
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• pp.167-172
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• 2004

Neural networks are used in the framework of sensor based tracking control of robot manipulators. They learn by practice movements the relationship between PSD(an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple back propagation networks one of which is selected according to which division (Corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.4
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• pp.82-90
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• 2009

Pneumatic cylinder is one of the low cost actuation sources which have been applied in industrial and prosthetic application since it has a high power/weight ratio, a high-tension force and a long durability However, the control problems of pneumatic systems, oscillatory motion and compliance, have prevented their widespread use in advanced robotics. To overcome these shortcomings, a number of newer pneumatic actuators have been developed such as McKibben Muscle, Rubber Actuator and Pneumatic Artificial Muscle (PAM) Manipulators. In this paper, one solution for position control of a robot arm, which is driven by two pneumatic artificial muscles, is presented. However, some limitations still exist, such as a deterioration of the performance of transient response due to the changes in the external load of the robot arm. To overcome this problem, a switching algorithm of the control parameter using a learning vector quantization neural network (LVQNN) is proposed in this paper. This estimates the external load of the pneumatic artificial muscle manipulator. The effectiveness of the proposed control algorithm is demonstrated through experiments with different external working loads.

• Proceedings of the KWS Conference
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• 2006.10a
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• pp.101-103
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• 2006

DSME has developed Sub-assembly Welding Robot System(SWRS) in order to increase the productivity of arc welding and to improve hazard and unclean environments in shipbuilding. DSME's SWRS includes a number of equipments such as four overhanging 6-axis articulated robot manipulators(10kg pay-load), gantry system, vision system detecting the workpiece automatically, and OLP system using the CAD data and a central control system integrating an anti-collision module. The SWRS was installed in CAS(Component Assembly Shop) of DSME's OKPO shipyard in August 2006, and now SWRS is running well in site.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.1
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• pp.18-28
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• 1992

시스템의 모델링 과정에서 발생될 수 있는 불확실성(uncertainty) 혹은 미지의 가반중량을 비롯한 외란에 의해 발생되는 불확실성 등을 갖고있는 로봇의 강건 추적제어기 설계를 위해 가변구조시스템(variable structure system) 이론을 적용하였다. 시스템 방정식과 연계하여 슬라이딩 모드가 존재하기 위한 조건을 구했으며, 입력 에 대한 불확실성은 매칭조건(matching condition)을 가정하여 다루었다. 기존의 방법에 비해 제어기 설계과정이 간단 명료하며 요구되는 궤적에 대한 추적제어 효과 또한 매우 우수함을 컴퓨터 시뮬레이션을 통해 입증하였다.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.3
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• pp.316-325
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• 1991

This paper presents the design of a new sliding mode controller to improve the rate of convergence by Lyapunov's stability analysis. The proposed controller shows that the elimination of the steady state position errors can be achieved by replacing the desired trajectory by the virtual reference trajectory. The proposed control scheme which consists of the upper bounded and estimated values of eac term of the manipulator dynamic equation does not require good knowledge of the parameters and the computation of matrix inversion. The performance of proposed controller is evaluated by the simulation for a two-link manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.147.2-147
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• 2001

In this paper we develop a new robust trajectory tracking control scheme without using joint velocity. The proposed controller doesn´t employ adaptation, Therefore, the construction of the controller becomed very simple. Moreover, by using numerical simulation, we make sure the effectiveness of the proposed controller in the presence of quantization errors.

• Journal of the Korean Society of Industry Convergence
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• v.20 no.4
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• pp.299-305
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• 2017

This study proposes a new technique to design and control of robot hand gripper for assembling and disassembling of a machine parts. The motion equation describing dynamics of the manipulators and object together with geometric constraint is formulated by Lagrange-Euler's equation. And the problems of controlling both the grasping force and the rotation angle of the grasped object under the constraints are analyzed. The effect of geometric constraints and a method of computer simulation for overall system is verified. Finally, it is illustrated that even in case of there exists a sensory feedback from sensing data of the rotational angle of the object to command inputs control of joint and this feedback connection from sensing data to control grasping of machinery parts.