• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.135-139
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• 1992

Control of tool-environment interaction force to comply the robot system to an environment is of vital in many automated process. This paper presents the implementation of an adaptive force control with commercial robot system in two dimensional contour following task. A model reference adaptive control system, combined with the linear compensators, is implemented. That is, a use of adaptive control is to provide an auxiliary control system so that the contour following performance can be improved from that of using linear control system only. Hyperstability is used to derive the adaptive control law. Experimental verification of the proposed control system is obtained using PUMA 560 robot system. Data obtained experimentally shows that the use of additional adaptive control system improves the contour following performance about 30% in RMS contact force errors upon that of the system controlled by the linear compensators only.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.404-407
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• 1995

Teleoperating system has been developed for several decades, and many control schemes for it have been suggested. But the implementation for real application needs very simple but effective controller. In this paper, an advanced control scheme for this purpose is suggested, which is the combination of a modified internal model controller and variable filter for force reflection. And we verify the effectiveness of the proposed scheme through the experiment. We use PUMA-560 as the slave robot, which is operated by velocity servo loop with geared motor. Both the responses of free motion and contact motion are shown.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.980-984
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• 1990

In order to find the optimal control law for the precise trajectory tracking of a robot manipulator, a perturbational control method is proposed based on a linearized manipulator dynamic model which can be obtained in a very compact and computationally efficient manner using the dual number algebra. Manipulator control can be decomposed into two parts: the nominal control and the corrective perturbational control. The nominal control is precomputed from the inverse dynamic model using the quantities of a desired trajectory. The perturbational control is obtained by applying the second-variational method on the linearized dynamic model. Simulation results for a PUMA-560 robot show that, by using this controller, the desired trajectory tracking performance of the robot can be achieved, even in the presence of large initial positional disturbances.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.12
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• pp.1652-1662
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• 1995

In this paper, a hybrid neural network is proposed to improve the learning ability of a neural network. The union of the characteristics of a Self-Organizing Neural Network model and of multi-layer perceptron model using the backpropagation learning method gives us the advantage of reduction of the learning error and the learning time. In learning process, the proposed hybrid neural network reduces the number of nodes in hidden layers to reduce the calculation time. And this proposed neural network uses the fuzzy feedback values, when it updates the responding region of each node in the hidden layer. To show the effectiveness of this proposed hybrid neural network, the boolean function(XOR, 3Bit Parity) and the solution of inverse kinematics are used. Finally, this proposed hybrid neural network is applied to the visual tracking control of a PUMA560 robot, and the result data is presented.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.1-6
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• 1990

Neural network is a computational model of ft biological nervous system developed ID exploit its intelligence and parallelism. Applying neural networks so robots creates many advantages over conventional control methods such as learning, real-time control, and continuous performance improvement through training and adaptation. In this paper, dynamic control of a six-link robot will be presented using neural networks. The neural network model used in this paper is the backpropagation network. Simulated control of the PUMA 560 am shows that it can move a high speed as well as adapt to unforseen load changes and sensor noise. The results are compared with the conventional PD control scheme.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.485-491
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• 1990

We propose a minimum-time path planning soheme for the robot manipulator using Hopfield neural network. The minimum-time path planning, which can allow the robot system to perform the demanded tasks with a minimum execution time, may be of consequence to improve the productivity. But most of the methods proposed till now suffers from a significant computational burden and thus limits the on-line application. One way to avoid such a difficulty is to apply the neural network technique, which can allow the parallel computation, to the minimum-time problem. This paper propose an approach for solving the minimum-time path planning by using Hopfield neural network. The effectiveness of the proposed method is demonstrarted using the PUMA 560 manipulator.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.500-503
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• 1990

Neural network is a computational model of the biological nervous system developed to exploit its intelligence and parallelism. Applying neural networks to robots creates many advantages over conventional control methods such as learning, real-time control, and continuous performance improvement through training and adaptation. In this paper, dynamic control of a six-link robot will be presented using neural networks. The neural network model used in this paper is the backpropagation network. Simulated control of the PUMA 560 arm shows that it can move at high speed as well as adapt to unforseen load changes. The results are compared with the conventional PD control scheme.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.10
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• pp.116-126
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• 2001

This paper suggests a numerical method to find optimal geometric path and minimum-time motion for a spatial 6-link manipulator arm (PUMA 560 type). To find a minimum-time motion, the optimal geometric paths minimizing 2 different dynamic performance indices are searched first, and the minimum-time motions are searched on these optimal paths. In the algorithm to find optimal geometric paths, the objective functions (performance indices) are selected to minimize joint velocities, actuator forces or the combinations of them as well as to avoid one static obstacle. In the minimum-time algorithm the traveling time is expressed by the power series including 21 terms. The coefficients of the series are obtained using nonlinear programming to minimize the total traveling time subject to the constraints of velocity-dependent actuator forces.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.430-434
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• 1987

This paper describes implementation of a robot programming system for automation of manufacturing products, which is embedded in the C programming language. World representation and motion primitives necessary to describe a manipulator task are provided by a set of procedure calls and user defined data structures. Off-line programming is implemented with graphic simulation as a debugging tool. PUMA 560 manipulators are used as a model for one task which inserts a nonstandard power IC Iota PCB. Communication facilities are provided for collision avoidance or two manipulators.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.2
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• pp.144-151
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• 1988

Between any two points in the Cartesian space, the straight motion has many distinct advantages in path/trajectory plannings, specially in a crowded workspace. But to achieve this motion is a nontrivial task. One standard way to approximate this motion is to plae enough intermediate points along the desired path and linearly interpolate two adjacent intermediate points in the joint space. In this approach, however, the determination of the minimum number of intermediate points is very important from several aspects. A scheme that can effectively accomplish this purpose is established in this paper. This scheme is based on several search methods. The results are demonstrated using the PUMA 560 series manipulator.