• 제어로봇시스템학회논문지
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• 제2권4호
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• pp.312-317
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• 1996

This paper proposes a neural network controller with the computed torque method. The neural network is used not to learn the inverse dynamic model but to compensate the uncertainties of robotic manipulators. When training the neural network, we use the signals present in the proposed controller, which is simpler than that proposed by Ishiguro et al., whose teaching signals of the neural network come from the robot model.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2007년도 춘계학술대회A
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• pp.1201-1206
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• 2007

Robot simulation technique is essential not only for robot developers to design robotic systems but also for robot operators to predict robot motion, configure system layout, and increase robot ability. However, commercial robot simulation software such as ROBCAD, IGRIP, and so on are expensive and sometimes they are difficult to customize into industrial purpose programming for users. Therefore, user-based simulation programming is required to magnify the efficiency of robot system. In this paper, we show the methodology of developing user-based robot simulation programming using PC(personal computer), Open-Inventor, and Windows Programming. The developed programming has been successfully applied to welding robot systems of a shipbuilding industry. Also, the methodology presented here can be easily extended to simulate manipulators of other typed mechanism on user's PC.

• 한국정밀공학회지
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• 제16권2호통권95호
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• pp.5-14
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• 1999

The necessity of six axis force-torque sensors is well recognized in the fields of automatic fine assembly, deburring polishing, and automatic fish processing using robotic manipulators. The paper proposes a simple and compact elastic structure of the force-torque sensor which senses externally applied three force and three torque components. Rough surface strain distribution of the elastic structure is examined analytically, and then more accurate surface strain are obtained from finite element analysis. The compliance matrix which is a linear relationship between force components and strain measurements is obtained for the proposed sensor. Some basic principles of measuring 3 force and torque components are also presented.

• 대한기계학회논문집A
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• 제21권11호
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• pp.1809-1818
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• 1997

Mathematical models of industrial robots or manipulators are highly nonlinear equations with nonlinear coupling between the variables of motion. As the working speed has been fast, the effects of nonlinear terms have become serious. So the control algorithm based on approximately linearized equation looses the efficiency. In order to design the control law for the nonlinear models, Hunt-Su's nonlinear transformation method and Marino's feedback equivalence condition are used with linear quadratic regulator(LQR) theory in this study. Nonlinear terms of the system are eliminated and coupled terms are decoupled by this feedback law. This method is applied to a 3-D.O.F. vertical articulated manipulator by both experiments and simulations and compared with PID control which is widely used in the industry.

• 대한전기학회논문지
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• 제39권4호
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• pp.414-427
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• 1990

This paper presents the development of the graphic simulator for an assembly workcell. The assembly workcell consists of two PUMA 560 manipulators, a conveyor belt system, a work table, and a vision sensor. In this study, the Petri Net theory is applied to model the assembly workcell and to construct the simulator. The event scheduling approach is used to simulate the cell. In order to show the graphic display of the simulation process, robot modelling, component modelling, and world modelling are included. The developed simulator is used to display the transition of the system state during the simulation. It is also used as a tool in selection the best resource states by studying the performance of the system as the resource states are changed.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.1042-1046
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• 1993

This paper describes a control scheme for a robot manipulator system which uses visual information to position and orientate the end-effector. In this scheme, the position and orientation of the target workpiece with respect to the base frame of the robot are assumed to be unknown, but the desired relative position and orientation of the end-effector to the target workpiece are given in advance. The control scheme directly integrates visual data into the servoing process without subdividing the process into determination of the position and orientation of the workpiece and inverse kinematics calculation. A neural network system is used for determining the change in joint angles required in order to achieve the desired position and orientation. The proposed system can be control the robot so that it approach the desired position and orientation from arbitrary initial ones. Simulation for the robot manipulator with six degrees of freedom will be done. The validity and the effectiveness of the proposed control scheme will be verified by computer simulations.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1987년도 한국자동제어학술회의논문집(한일합동학술편); 한국과학기술대학, 충남; 16-17 Oct. 1987
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• pp.886-891
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• 1987

In this paper, we present a effective method to control robotic systems by an iterative learning algorithm. This method is based on the concepts of the learning control law which is introduced in this paper, that is, avoidance of using derivative of system state and ignorance of high frequency influence in system performance. By means of the betterment of performance due to the improvement of estimated unknown information, the learning control algorithm compels the system to gradually approach in desired trajectory, and eventually the tracking error asymptotically converges upon zero. In order to verify its utility, one degree of freedom of manipulator has been used in the experiments and the results illustrate this control scheme is very effective.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1989년도 추계학술대회 논문집 학회본부
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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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1992년도 하계학술대회 논문집 A
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• pp.401-403
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• 1992

For the control of robotic manipulators with unknown or uncertain dynamics, leaming control schemes are very effective control schemes for repeated trajectory following tasks. In this class of controllers, control techniques using neural networks have been gaining much attention in recent years.. In this note, we discuss the leaming control techniques using neural networks and propose a new model-based control scheme using multilayered neural networks. Three-layerd neural network is used as a feedback controller to compensate the mismatched terms between model plant and real plant. Computer simulations are performed to show the applicability and the limitation of the proposed controller.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 춘계학술대회 논문집
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• pp.235-240
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• 1995

In order to eliminate position errors existing at the steady state in the motion control of robotic manipulators, a new fuzzy control algorithm is proposed using three variables, position error, velocity error and integral of position errors as input variables of the fuzzy controller. Three dimensional look-up table is used toreduce the computational time in real-time control, and a technique reducing the amount of necessary memory is introduced. Simulation and experimental studies show that the position errors at the steady state are decreased more than 90% compared to those of existing fuzzy controller when the proposed fuzzy controller is applied to the 2 axis direct drive SCARA robot manipulator.