• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.122-127
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• 2001

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.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.256-261
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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-neuro control scheme is proved to be a efficient control technique for real-time control of robot system using DSPs.

• Journal of Advanced Marine Engineering and Technology
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• v.30 no.8
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• pp.934-940
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• 2006

The aims of this study is to develop working robot for a installed trolley cable of an electric train and objective of this paper is to implement mobile control of working robot. In this paper an approach to method for scheme of a mobile control system is presented in a dynamic hybrid velocity/tension control of working robot. The working robot is composed the velocity and tension controllers using the concept of two-degrees-of-freedom servo-controller. This robot moved at same time a certain distance to constrain a constant tension and installed a trolley cable of an electric train. To move the robot the velocity control system have design and implemented. Simulation and experimental results are presented to illustrate the validity of designed mobil scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.3
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• pp.252-258
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• 1997

This paper presents neural networks for hybrid position/force control which is a type of position and force control for robot manipulators. The performance of conventional hybrid position/force control is excellent in the case of the exactly-known dynamic model of the robot, but degrades seriously as the uncertainty of the model increases. Hence, the neural network control scheme is presented here to overcome such shortcoming. The introduced neural term is designed to learn the uncertainty of the robot, and to control the robot through uncertainty compensation. Further more, the learning rule of the neural network is derived and is shown to be effective in the sense that it requires neither desired output of the network nor error back propagation through the plant. The proposed scheme is verified through the simulation of hybrid position/force control of a 6-dof robot manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.669-674
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• 1989

Robot manipulators are highly coupled nonlinear systems and their motions are influenced by uncertain dynamics. In this paper a design methodology which is called model feedback control system or plant model control scheme is presented for the purpose of reducing the influence of the uncertain dynamics. This control system is applied to the trajectly control of the directly drived robot. Theoretically and experimentally performances resulting from use of this control scheme show that the influences of the uncertain dynamics are reduced obviously.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.2
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• pp.126-132
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• 2002

Robot manipulator has highly nonlinear dynamics. Therefore the control of multi-link robot arms is a challenging and difficult problem. In this paper an independent joint adaptive fuzzy sliding mode scheme is developed leer control of robot manipulators. The proposed scheme does not require an accurate manipulator dynamic model, yet it guarantees asymptotic trajectory tracking despite gross robot parameter variations. Numerical simulation for independent joint control of a 3-axis PUMA arm will also be included.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.5
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• pp.489-495
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• 2008

This paper offers a practical control scheme for driving mode transformation of a rescue robot already developed. The rescue robot, VSTR(Variable Single-Tracked Robot), has two driving modes, so can traverse untidy terrain and overcome obstacles such as stairs easily by use of timely driving mode transformation. Classical PI control scheme was used firstly for driving mode transformation, but stationary phenomenon, which might have a bad effect on the performance in real situation, came into existence. Therefore, we suggest a new controller, PI-based feedforward controller, which should be a good alternative for the problem, and compare it with other nonlinear control scheme.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.657-660
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• 2003

Control of multi-link robot arms is a very difficult problem because of the highly nonlinear dynamics. Decentralized control scheme is developed for control of robot manipulators based on RBF(Radial Basis Function) Neural Networks. RBF Neural Networks is used to approximate the coupling forces among the joints, coriolis force, centrifugal force, gravitational force, and frictional force. The compensation controller is also proposed to estimate the bound of approximation error so that the chattering effect of the control effort can be reduced. The proposed scheme does not require an accurate manipulator dynamic, and it is proved that closed-loop system is asymptotic stable despite the gross robot parameter variations. Numerical simulations for two-link robot manipulator are included to show the effectiveness of controller.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.12
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• pp.1033-1041
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• 2003

A new visual tracking scheme is proposed for a mobile robot that tracks a moving object in 3D space in real time. Visual tracking is to control a mobile robot to keep a moving target at the center of input image at all time. We made it possible by simplifying the relationship between the 2D image frame captured by a single camera and the 3D workspace frame. To precisely calculate the input vector (orientation and distance) of the mobile robot, the speed vector of the target is determined by eliminating the speed component caused by the camera motion from the speed vector appeared in the input image. The problem of temporary disappearance of the target form the input image is solved by selecting the searching area based on the linear prediction of target motion. The experimental results have shown that the proposed scheme can make a mobile robot successfully follow a moving target in real time.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.4
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• pp.54-64
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• 1997

This paper provides a practical control scheme called three-step input method in order to minimize both robot response time and the resulting residual vibration when the robot manipulator reaches its defined end point. This work is concerned with defining a simple practical method to utilize step inputs to achieve optimum response. The optimum response is achieved by using a self- adjusting input command function that is obtained during a real time processing . The practicality of this control scheme is demonstrated by using an analog computer to simulate a simulate a simple flexible robot and conventional servo controller. The experiments focus on point-to-point movement. Also, this method requires little computational effort through the intelligent use of conventional servo control technology and the robot's vibration characteristics.