• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.1 s.307
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• pp.13-20
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• 2006

In this paper, we propose a motion control scheme of robot manipulators based on visual feedback under camera-in-hand configuration. The desired joint velocity and acceleration for motion control is made by the feature-based visual data in the outer loop. The control input for tracking feature points on the image plane uses robot kinematics dynamic. The proposed control input consists of the image feature and the joint velocity error to achieve robustness to the parametric uncertainty. The stability of the closed-loop system is proved by Lyapunov approach. Computer simulations and experiments on a two degree of freedom manipulator with 5 links are presented to illustrate the performance of proposed control system.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.648-653
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• 1993

This paper presents an nonlinear observer scheme for flexible joint robot manipulators. This nonlinear observer scheme is based on the sliding mode method. Sliding controllers have recently been shown to feature excellent robustness and performance properties for specific classes of nonlinear tracking problems. Dynamic equations of flexible joint robot manipulators are derived from the Euler-Lagrange equations by forming the corresponding Lagrangian. Simulation results are presented to show the validness of the proposed nonlinear observer scheme.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.597-600
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• 1994

A Robot system to realize a painting using a writing brush is explained here. Based on the three-dimensional data about the target china, the movements of the writing brush is determined. The movement is realized by the movement of two robot manipulators which move coordinatedly. Experimental results reveals the applicability of one system.

• Proceedings of the KAIS Fall Conference
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• 2008.11a
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• pp.191-196
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• 2008

The intent of this paper is to describe a neural network structure called dynamic neural processor(DNP), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the DNP, are described. Computer simulations are provided to demonstrate the effectiveness of the proposed learning using the DNP.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.255-260
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• 2001

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.10a
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• pp.133-137
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• 1996

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have bevome increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking arre indispensable capabilities for their versatile application. the need to meet demanding control requirement in increasingly complex dynamical control systems under sygnificant uncertainties leads toward design of implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme the ntworks intrduced are neural nets with dynamic neurouns whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure fast in computation and suitable for implementation of real-time control, Performance of the neural controller is illustrated by simulation and experimental results for a SCAEA robot.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.117-122
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• 1998

The recent developments and studies in the framework of output tracking control in the field of robotics that has been studied in the Control Laboratory, are presented. An output controller based on“Hardware-ln-the-Loop Simulation”(HILS) and“Rapid Control Prototyping”(RCP) concepts is developed using dSPACE. These new concepts are shown to be particularly beneficial for manipulator control tasks. In the Elbow manipulator design, there are two kinds of manipulators, namely the serial-drive type and the parallelogram-drive manipulator, The objective of this research is to model the two Elbow manipulators and to implement the proposed controller for manipulator applications. The control goal is to force the manipulator to follow a given trajectory in the given work space. Output controllers of the two elbow manipulators that are based on the model matching control approach have been implemented in two models that represent the robot equations of motion. To reduce the efforts in evaluating the proposed algorithm, a new system configuration method, based on HILS and RCP tools, was suggested to determine the parameters of the integrated dynamic system.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.4
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• pp.312-316
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• 2004

In this paper, it is presented an adaptive robust control system to implement real-time control of a robot manipulator. There are Quantitative or qualitative differences between a real robot manipulator and a robot modeling. In order to compensate these differences, uncertain factors are added to a robot modeling. The uncertain factors come from imperfect knowledge of system parameters, payload change, friction, external disturbance, etc. Also, uncertainty is often nonlinear and time-varying. In the proceeding work, we proposed a class of robust control of a robot manipulator and provided the stability analysis. In the work, we propose a class of adaptive robust control of robot manipulator with bound estimation. Through experiments, the proposed adaptive robust control scheme is proved to be an efficient control technique for real-time control of a robot system using DSP.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1569-1572
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• 1997

In this paper, we present an associative memory network (AMN) controller for dynamic robot control. The purpose of using AMN is to reduce the size of required memory in storing and recalling large of daa representing input relationship of nonlinear functions. With the capability AMN can be used to dynamic robot control, which has nonlinear properties inherently. The proposed AMN control scheme has advantages for the inverse dynamics learning no limitatiion of inpur range, and insensitive of payload change. Computer simulations show the effectiveness and feasibility of proposed scheme.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.560-563
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• 1997

The conventional impedance control has been known to have the following problems: it has lack of specifying force directly and unknown environment stiffness has to be known priori in order to specify the reference trajectory. In this paper, new impedance force control that can control a desired force directly under unknown stiffness is proposed. A new nonlinear impedance function is developed based on estimation of unknown stiffness from force and position measurements. The nonlinear characteristics of the proposed impedance function are analyzed based on unknown environment position. Simulation studies with robot manipulator are carried out to test analytical results.