• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.330-335
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• 1995

The actuator's response delay, disturbance and measurement noise can often cause a significant error in the target tracking of an underwater vehicle. The first purpose of this paper is error analysis about motion of an underwater vehicle when the closed loop system has actuator and disturbance and noise. The underwater vehicle is simulated for cases of various disturbances. The second purpose is robust controller design for the underwater vehicle with parameter uncertainty. So, two robust control methods are applied for the underwater vehicle. One is standard $H_{\infty}$ control, and the other is time-varying sliding mode control with modified saturation function. Suboptimal design parameters for $H_{\infty}$ control, and design parameters for time-varying switching surfaces are provided Simulations for the two controllers are carried out and their performances are analyzed.lyzed.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.7 no.1
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• pp.13-19
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• 1995

An experimental investigation has been made for flow in a circular cylinder with a rotating bottom disk. Flow system considered in this paper is a characteristic model of interior flows of an electric washing machine. Flows in a tub of an electric washing machine are driven by a rotating bottom disk called a pulsator. The simple and characteristic model was composed of a circular cylinder with impulsively rotating endwall disk and a viscous fluid in it. Rotational motion of the pulsator is periodic and alternative in rotation direction. The flow field in the interior region is governed by a horizontal boundary layer forms on the impulsively rotating disk. Experimental approach was accomplished by adopting an image processing technique for velocity measurements. Comprehensive details of the flow structure are presented. Also a meridional circulation is obtained by tracking image particles suspended in the fluid. Flow structure and data are successfully procured for this complex rotating flow field.

• Journal of Industrial Technology
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• v.29 no.A
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• pp.47-54
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• 2009

In this paper, we proposed hand detection and hand gesture recognition from USB camera video. Firstly, we extract hand region extraction using skin color information from a difference images. Background image is initially stored and extracted from the input images in order to reduce problems from complex backgrounds. After that, 16-directional chain code sequence is computed from the tracking of hand motion. These chain code sequences are compared with pre-trained models using DP matching. Our hand gesture recognition system can be used to control PowerPoint slides or applied to multimedia education systems. We got 92% hand region extraction accuracy and 82.5% gesture recognition accuracy, respectively.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.03a
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• pp.79-84
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• 1996

This paper presents a new approach to the design of adaptive control system using DSPs(TMS320C31) for robotic manipulators to achieve trajectory tracking by the joint angles. Digital signal processors are used in implementing real time adaptive control algorithms to provide an enhanced motion control for robotic manipulators. In the proposed control scheme, adaptation laws are derived from the improved Lyapunov second stability analysis method based on the adaptive model reference control theory. The adaptive controller consists of an adaptive feedforward controller, feedback controller, and PID type time-varying auxillary control elements. The proposed adaptive control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Moreover, this scheme does not require an accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the adaptive controller is illustrated by simulation and experimental results for a SCARA robot.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.5 no.4
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• pp.26-37
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• 1996

This paper presents a new approach to the design of adaptive control system using DSPs(TMS320C30) for robotic manipulators to achieve trajectory tracking by the joint angles Digital signal processors are used in implementing real time adaptive control algorithms to provide an enhanced motion control for robotic manipulators. In the proposed control scheme adaptation laws are derived from the improved Lyapunov second stability analysis method based on the adaptive model reference control theory. The adaptive controller consists of an adaptive feedforward controller. feedback controller. and PID type time-varying auxiliary control elements. The proposed adaptive control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Moreover, this scheme does not require a an accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the adaptive controller is illustrated by simulation and experimental results for a SCARA robot.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.12
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• pp.967-972
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• 2001

In this paper, an adaptive robust nonlinear predictive controller is developed for the continuous time nonlinear systems whose control objective is composed of the system output and its desired value. The basic control law is derived from the continuous time prediction model and its feedback dynamcis shows another from if input and output linearization. In order to cope with the parameter uncertainty, robust control is incorporated into the basic control law and the asymptotic convergence of tracking error to a certain bounded region is guaranteed. For stability and performance improvement within the bounded region, an adaptive control is introduced. Simulation tests for the motion control of an underwater wall-ranging robot confirm the performance improvement and the robustness of this controller.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.6
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• pp.417-422
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• 2005

This paper presents an EMG pattern classification method to identify motion commands for the control of the artificial arm by SOM-TVC(self organizing map - tracking Voronoi cell) based on neural network with a feature parameter. The eigenvalue is extracted as a feature parameter from the EMG signals and Voronoi cells is used to define each pattern boundary in the pattern recognition space. And a TVC algorithm is designed to track the movement of the Voronoi cell varying as the condition of additive noise. Results are presented to support the efficiency of the proposed SOM-TVC algorithm for EMG pattern recognition and compared with the conventional EDM and BPNN methods.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2011-2013
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• 2001

In this paper the MMAE(Multiple Model Adaptive Estimation) algorithm using the MGEKF(Modified Gain Extended Kalman Filter) of which modes are set to be measurement biases is proposed to enhance the performance of target tracking with bearing only measurements. The state are composed of relative position, relative velocity and taregt acceleration. The mode probability is calculated from the bearing only measurements from the HMS(Hull-Mounted Sonar). The proposed algorithm is tested in a series of computer simulation runs.

• Journal of Drive and Control
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• v.11 no.2
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• pp.9-15
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• 2014

This paper deals with the issue of force synchronizing control for the clamping servomechanism of injection molding machines. Prior to the controller design, a virtual design model has been developed for the clamping mechanism with hydraulic systems. Then, a synchronizing controller is designed and combined with an adaptive feedforward control in order to accommodate the mismatches between the real plant and the linear model plant used. As a disturbance, the leakage due to the ring gap with relative motion in the cylinder has been introduced. From the robust force tracking simulations, it is shown that a significant reduction in the force synchronizing error is achieved through the use of a proposed control scheme.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.69.2-69
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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.