• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.990-993
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• 1988

RMFC (Resolved Motion Force Control) is the method to control the Cartesian force and position using FCC (Force Convergent Control) instead of the complicated dynamic equations of the manipulator. The gain parameters of the controller are adjusted through many trial and errors. In this paper PD-optimal control method is introduced to give optimal gain parameters which minimize the difference between actural acceleration and desired acceleration. To show the validitiesn of the proposed method computer simulations are performed for the two-link manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2004-2009
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• 2003

A robust optimal control design is proposed in this study for rigid robotic systems under the unknown load and the other uncertainties. The uncertainties are quadratically bounded for some positive definite matrix. Iterative method finding the Q weighting matrix is shown. Computer simulations have been done for a weight-lifting operation of a two-link manipulator and the result of the simulation shows that the proposed algorithm is very effective for a robust control of robotic systems.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.455-458
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• 2003

An optimal control approach to robust control design is proposed in this study for rigid robotic systems under the unknown load and the other uncertainties. The uncertainties are quadratically bounded for some positive definite matrix. Iterative method to find the matrix is shown. Simulations arc made for a weight-lifting operation of a two-link manipulator and the robust control performance of robotic systems by the proposed algorithm is remarkable.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.230-235
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• 1992

We propose a method of generating data to train a neural network controller. The data can be prepared directly by an iterative learning technique which repeatedly adjusts the control input to improve the tracking quality of the desired trajectory. Instead of storing control input data in memory as in iterative learning control, the neural network stores the mapping between the control input and the desired output. We apply this concept to the trajectory control of a two link robot manipulator with a feedforward neural network controller and a feedback linear controller. Simulation results show good generalization of the neural network controller.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.12
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• pp.176-182
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• 2003

An optimal control approach to robust control design is proposed in this study for rigid robotic systems under the unknown load and the other uncertainties. The uncertainties are quadratically bounded for some positive definite matrix. Iterative method to find the matrix is shown. Simulations are made for a weight-lifting operation of a two-link manipulator and the robust control performance of robotic systems by the proposed algorithm is remarkable.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1222-1224
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• 1996

The Hong Ik Direct Drive Arm(HIDDA) is a SCARA typed direct drive manipulator with two degrees-of-freedom(DOF) using the direct drive motor of the NSK company. The direct NSK motors are used to give a large torque directly to the link, to reduce the modeling errors from the gears and chains. But, since the nonlinear coupling torques are transferred to the motor shaft without any reduction, we must consider a dynamic control algorithm. In this paper, we designed a robot controller for the HIDDA using a TMS320C31, which has the highest performance among the third DSP chips in the TI company. And we developed the integrated environment software of the robot management system to give the users an easy way of programming, running and simulation of the robot on the PC.

• Journal of Bio-Environment Control
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• v.13 no.1
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• pp.1-7
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• 2004

This study is kinematics for the manipulator development of cucumber harvesting. A theory value was verified by repeated error measurement after the forward kinematics or inverse kinematics analysis of manipulator. Manipulator is consisted of one perpendicular link and two revolution link. The transformation of manipulator can be valued by kinematics using Denavit-Hartenberg parameter. The value of inverse kinematics which is solved by three angles faction shows two types. Repeated errors refered maximum 2.60 mm, 2.05mm and 1.55 mm according to X, Y, Z axis. In this study, the actual coordinates of maximum point and minimum point were agreement in the forward kinematics or inverse kinematics. The results of repeated error measurement were reflect to be smaller compared to a diameter of cucumber. measurement errors were determined by experimented errors during the test. For reducing errors of manipulator and improving work efficiency, the number of link should be reduced and breeding and cultural environment should be considered to reduce the weight and use the hard stuff. The velocity of motor for working should be considered, too.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.1
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• pp.38-44
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• 2001

This paper investigates the direct adaptive control of nonlinear systems using RBFN(radial basis function networks). The structure of the controller consists of a fixed PD controller and a RBFN controller in parallel. An adaptation law for the parameters of RBFN is developed based on the Lyapunov stability theory to guarantee the stability of the overall control system. The filtered tracking error between the system output and the desired output is shown to be UUB(uniformly ultimately bounded). To evaluate the performance of the controller, the proposed method is applied to the trajectory contro of the two-link manipulator.

• Journal of Electrical Engineering and information Science
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• v.1 no.1
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• pp.39-44
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• 1996

In this paper, adaptive controller with switching action is designed for rigid body robot manipulators to ensure the uniform stability of the manipulator system without a priori knowledge of the unmodeled dynamics. It will be shown that the parameter estimates are bounded independent of the other closed-loop signals boundedness, and also shown that the tracking error belongs to the normalized error bound via mathematical analisys. The robustness and performance of the proposed adaptive controller is investigated for the two-link direct drive manipulator actuated by VRM(Variable Reluctance Motor).

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.2
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• pp.88-95
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• 1996

It is inevitable for local systems to have deviations which represent interactions and modeling errors originated from the decomposition process of a large scale system. This paper presents a decentralized control scheme for interconnected systems using local linear models and a neuro-coordinator. In the proposed method, the local system is composed of a linear model and unknown deviations caused by linearizing the subsystems around operating points or by estimating parameters of the subsystems. Because the local system has unmeasurable deviations we define a local reference model which consists of a local linear model and a neural network to estimate the deviations indirectly. The reference model is reformed into a linear model which has no deviations through a transformation of input variables and we obtain an optimum feedback control law which minimizes a local performance index. Finally, we derive a decentralized feedback control law which consists of local linear states and neural network outputs. In the decentralized control, the neuro-coordinator generates a corrective control signal to cancel the effect of deviations through backpropagation learning with the errors obtained from the differences of the local system outputs and reference model outputs. Also, the stability of local system is proved by the degree of learning of the neural network under an assumption on a neural network learning index. It is shown by computer simulations that the proposed control scheme can be applied successfully to the control of a biased two-link planar robot manipulator.