• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.63-66
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• 1988

In the control of the robotic manipulators, the variable structure control method for the get Point Regualation has a advantage of the insensitivity about parameter variations and disturbances. When the robotic manipulators are controlled by a point-to-point scheme, no path constraint is considered. Thus, the variable structure control method will be effectively applied only if the trajectory of the robot hand is estimated precisely. In this paper, the joint trajectories in the joint space and the hand trajectory in the cartesian space are calculated by the variable structure control method, and an algorithm is suggested to elaborate the deviation error of the robot hand from a straight line path. The result of this study will become a base of the effective path planning about robotic manipulators with the variable structure control concept.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.10
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• pp.37-45
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• 1992

For a precise manipulator control in the presence of environmental uncertainties, it has long been recognized that the robot should be controlled in a task-referenced space. In this respect, an effective visual servo control system for robot manipulators based on neural networks is proposed. In the proposed control system, a Backpropagation neural network is used first to learn the mapping relationship between the robot's joint space and the video image space. However, in the real control loop, this network is not used in itself, but its first and second derivatives are used to generate servo commands for the robot. Second, and Adaline neural network is used to identify the approximately linear dynamics of the robot and also to generate the proper joint torque commands. Computer simulation has been performed demonstrating the proposed method's superior performance. Futrhermore, the proposed scheme can be effectively utilized in a robot skill acquisition system where the robot can be taught by watching a human behavioral task.

• Journal of Drive and Control
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• v.14 no.4
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• pp.51-60
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• 2017

In this study to develop disaster response robot in complex disaster site, we present the design of hydraulic manipulators for armored robot systems. To this end, we performed voice of customer researches with firefighters and rescue personnel. We created and analyzed the mission scenario of firefighters and rescue personnel in complex disaster situations, and derived the required functions of the robot to successfully perform missions. A heavy-duty, heat resistant, dexterous hydraulic robot manipulators is designed to realize the required functions. The designed robot has been verified through simulations and analysis in terms of the working area of the robot, actuating torques, and temperature analysis.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.10
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• pp.1471-1478
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• 1989

The optimal-time control of the coordinated motion of two robot manipulators may be of consequence in the industrial automation. In this paper two robot manipulators garsping a common object are assumed to travel a specified Cartesian path and the method how to derive the optimal-time solution is explained. This approach is based on parameterizing the corresponding patn and utilizing the phase-plame technique in the trajectory planning. Also the torques supplied by the actuators are assumed to have some constant bounds. The effectiveness of this approach is demonstrated by a computer simulation using a PUMA 560 manipulator.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.2
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• pp.192-197
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• 2001

Recent requirements for the fast and accurate motion in industrial robot manipulators need more advanced control techniques. To satisfy the requirements, importance of the force control is being continuously increased and the expensive force sensor is often installed to obtain the contact force information in practice. This information is indispensable for the force control of maintaining the desired contact force. However, the sensor cost is too high to be used in industrial applications. In this paper, it is proposed to estimate the contact force occurred between the end-effector of robots and environment in 3-D. The contact force monitoring system is developed based on the static and dynamic models of 3 DOF robot manipulators, where the contact force is described with respect to the link torque. The Extended Kalman Filter is designed and its performance is verified in simulations.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.5
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• pp.551-561
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• 1992

A neural optimization network and fuzzy rules are proposed to control the redundant robot manipulators in an environment with obstacle. A neural optimization network is employed to solve the optimization problem for resolved motion control of redundant robot manipulators in an environment with obstacle. The fuzzy rules are proposed to determine the weights of neural optimization networks to avoid the collision between robot manipulators and obstacle. The inputs of fuzzy rules are the resultant distance and change of the distance and sum of the changes by differential motion of each joint. And the output of fuzzy rules is defined as the capability of collision avoidance of joint differential motion. The weightings of neural optimization networks are adjusted according to the capability of collision aboidance of each joint. To show the validities of the proposed method, computer simulation results are illustrated for the redundant robot of the planar type with three degrees of freedom.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.113.1-113
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• 2001

The robust position control with the bound function of neural network structure is proposed for uncertain robot manipulators. The neural network structure presents the bound function and does not need the concave property of the bound function, The robust approach is to solve this problem as uncertainties are included in a model and the controller can achieve the desired properties in spite of the imperfect modeling. Simulation is performed to validate this law for four-axis SCARA type robot manipulators.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.259-262
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• 1997

This paper presents a visual servoing of RV-M2 robot manipulators to track and grasp moving object, using pruned dynamic recurrent neural networks(DRNN). The object is stationary in the robot work space and the robot is tracking and grasping the object by using CCD camera mounted on the end-effector. In order to optimize the structure of DRNN, we decide the node whether delete or add, by mutation probability, first in case of delete node, the node which have minimum sum of input weight is actually deleted, and then in case of add node, the weight is connected according to the number of case which added node can reach the other nodes. Using evolutionary programming(EP) that search the struture and weight of the DRNN, and evolution strategies(ES) which train the weight of neuron, we pruned the net structure of DRNN. We applied the DRNN to the Visual Servoing of a robot manipulators to control position and orientation of end-effector, and the validity and effectiveness of the pro osed control scheme will be verified by computer simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.294-299
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• 2006

This paper studies a visual feedback control scheme for robot manipulators with camera-in-hand configurations. We design a robust controller that compensates for bounded parametric uncertainties of robot mechanical dynamics. In order to reduce steady state tracking error of the robot arms due to uncertain dynamics, integral action is included in the control input. Using the Lyapunov stability criterion, the uniform ultimate boundedness of the tracking error is proved. Simulation and experimental results with a 2-1ink robot manipulator illustrate the robustness and effectiveness of the proposed control algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.2
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• pp.96-101
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• 2007

Neural networks are known as kinds of intelligent strategies since they have learning capability. There are various their applications from intelligent control fields; however, their applications have limits from the point that the stability of the intelligent control systems is not usually guaranteed. In this paper we propose an adaptive tracking control for robot manipulators using the radial basis function network (RBFN) that is e. kind of neural networks. Adaptation laws for parameters of the RBFN are developed based on the Lyapunov stability theory to guarantee the stability of the overall control scheme. Filtered tracking errors between actual outputs and desired outputs are discussed in the sense of the uniformly ultimately boundedness(UUB). Additionally, it is also shown that parameters of the RBFN are bounded. Experimental results for a SCARA-type robot manipulator show that the proposed adaptive tracking controller is adaptable to the environment changes and is more robust than the conventional PID controller and the neuro-controller based on the multilayer perceptron.