• Journal of Institute of Control, Robotics and Systems
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• v.4 no.4
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• pp.479-486
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• 1998

In this paper, the design and the implementation of a robust adaptive controller for trajectory tracking of robot manipulator is presented. The proposed control scheme ensures that tracking errors are converged to some boundaries in the presence of a state-dependent input disturbances as well as the ideal case without any prior knowledge of the robot manipulator parameters. The 3 DOF robot manipulator including actuator dynamics is used for the implementation of the proposed control scheme. The experimental results show that the proposed control scheme is valid for trajectory tracking of the robot manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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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.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.207-212
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• 2003

This paper proposed trajectory tracking control of Mobile Robot. Trajectory tracking control scheme are Real coding Genetic-Algorithm and Back-propergation Algorithm. Control scheme ability experience proposed simulation.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.181-186
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• 2004

This paper proposed trajectory tracking control of Mobile Robot. Trajectory tracking control scheme are Real coding Genetic-Algorithm and Back-propergation Algorithm. Control scheme ability experience proposed simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2024-2029
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• 2005

In this paper, it is shown how a mobile robot can navigate with high speed in dynamic real environment. In order to achieve high speed and safe navigation, a robot collects environmental information. A robot empirically memorizes locations of high risk due to the abrupt appearance of dynamic obstacles. After collecting sufficient data, a robot navigates in high speed in safe regions. This fact implies that the robot accumulates location dependent environmental information and the robot exploits its experiences in order to improve its navigation performance. This paper proposes a computational scheme how a robot can distinguish regions of high risk. Then, we focus on velocity control in order to achieve high speed navigation. The proposed scheme is experimentally tested in real office building. The experimental results clearly show that the proposed scheme is useful for improving a performance of autonomous navigation. Although the scope of this paper is limited to the velocity control in order to deal with unexpected obstacles, this paper points out a new direction towards the intelligent behavior control of autonomous robots based on the robot's experience.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.552-555
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• 1997

Recently, increasing attention has been paid to the application of learning control method to robot manipulator control. Because the learning control method does not require an exact dynamic model, it is flexible and easy to implement. In this paper, we implement a learning control scheme which consists of a unique feedforward learning controller and a linear feedback controller. The learning control method does not require acceleration terms that are sensitive to noise and has the capability of rejecting unknown disturbances and adapting itself to time-varying system parameters. The feasibility of the learning control scheme is soon by implementing the control scheme to a commercial robot manipulator and the performance of which is also compared with the conventional linear PID control method.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.219-226
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• 2003

In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator. Unlike the well-established theory fir the adaptive control of linear systems, there exists relatively little general theory fir the adaptive control of nonlinear systems. Adaptive control technique is essential fir providing a stable and robust performance fir application of robot control. The proposed neuro control algorithm is one of teaming a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-neuro control scheme is proved to be a efficient control technique f3r real-time control of robot system using DSPs.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.193-200
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• 2004

In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-neuro control scheme is proved to be a efficient control technique for real-time control of robot system using DSPs.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.260-265
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• 1992

This paper presents an adaptive control scheme for flexible joint robot manipulators. This control scheme is based on the Lyapunov direct method with the arm energy-based Lyapunov function. The proposed adaptive control scheme uses only the position and velocity feedback of link and motor shaft. The adaptive control system of flexible joint robots is asymptotically stable regardless of the joint flexibility value. Therefore, the assumption of weak joint ealsticity is not needed. Also, joint flexibility value is unknown. Simulation results are presented to show the feasibility of the proposed adaptive control scheme.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.573-578
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• 2002

In this paper, it Is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-negro control scheme is proved to be a efficient control technique for real-time control of robot system using DSPs.