• Journal of Institute of Control, Robotics and Systems
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• v.11 no.3
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• pp.262-269
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• 2005

Automatic landing of UAVs receives increasing interest these days, with increasing number of the developed UAV systems. In this paper, a glidepath tracking algorithm of the subscale UAV was proposed and the performance was analyzed. Flight data analysis shows that the existing autolanding flight control algorithm has a classical type glidepath control. This paper presents an alternative glidepath tracking strategy based on embedded flight control law. The performance of the proposed strategy was investigated through the TDP(Touch Down Point) error analysis with regard to various flight environment: steady headwind, atmospheric disturbance, communication transfer delay. It was verified that the proposed glidepath tracking strategy can be successfully applied to the practical autolanding of UAV systems.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.6
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• pp.1245-1254
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• 2011

This paper proposes a target tracking algorithm for wheeled mobile robots using in various fields. For the stable tracking, we apply a vision system to a mobile robot which can extract targets through image processing algorithms. Furthermore, this paper presents an algorithm to position the mobile robot at the desired location from the target by estimating its relative position and attitude. We show the problem in the tracking method using the Position-Based Visual Servo(PBVS) control, and propose a tracking method, which can achieve the stable tracking performance by combining the PBVS control with Image-Based Visual Servo(IBVS) control. When the target is located around the outskirt of the camera image, the target can disappear from the field of view. Thus the proposed algorithm combines the control inputs with of the hyperbolic form the switching function to solve this problem. Through both simulations and experiments for the mobile robot we have confirmed that the proposed visual servo control method is able to enhance the stability compared to of the method using only either PBVS or IBVS control method.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.6
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• pp.435-441
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• 2003

This paper addresses that an approximation and tracking control of realtime recurrent neural networks(RTRN) using two-dimensional iterative teaming algorithm for an electro-hydraulic servo system. Two dimensional learning rule is driven in the discrete system which consists of nonlinear output fuction and linear input. In order to control the trajectory of position, two RTRN with the same network architecture were used. Simulation results show that two RTRN using 2-D learning algorithm are able to approximate the plant output and desired trajectory to a very high degree of a accuracy respectively and the control algorithm using two identical RTRN was very effective to trajectory tracking of the electro-hydraulic servo system.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.350-355
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• 1990

A pressure tracking of hydroforming process is considered in this paper. To account for nonlinearities and uncertainty of the process. A fuzzy SOC based iterative learning control algorithm is proposed. A series of experimentals were performed for the pressure tracking control of the process. The experimental results show that regardless of inherent nonlinearties and uncertainties associated with hydraulic system. A good pressure tracking control performance is obtained using the proposed fuzzy learning control algorithm.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.207-212
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• 2003

This paper proposed trajectory tracking control of Mobile Robot. Trajectory tracking control scheme are Real coding Genetic-Algorithm and Back-propergation Algorithm. Control scheme ability experience proposed simulation.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.181-186
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• 2004

This paper proposed trajectory tracking control of Mobile Robot. Trajectory tracking control scheme are Real coding Genetic-Algorithm and Back-propergation Algorithm. Control scheme ability experience proposed simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.7
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• pp.536-542
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• 2016

To enhance tracking efficiency, a target-tracking filter with a resource management algorithm is required. One of the resource management algorithms chooses or evaluates the proper sampling time using cost functions which are related to the target tracking filter. We propose a resource management algorithm for bearing only tracking environments. Since the tracking performance depends on the system observability, the bearing-only tracking is one of challenging target-tracking fields. The proposed algorithm provides the adaptive sampling time using the variation rate of the error covariance matrix from the target-tracking filter. The simulation verifies the efficiency performance of the proposed algorithm.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2008.05a
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• pp.403-406
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• 2008

In this paper proposes a novel tracking algorithm regarding the power loss when operating a tracking system for a rapidly changing insolation to improve the power of PV tracking system. In case of tracking an azimuth and altitude of the sun in realtime, therefore, the actual PV power is less increasing than the power of tracking system fixed a specific position. To reduce the power loss, this paper proposes a nonel control algorithm of the tracking system. The paper is analyzed efficiency about conventional PV tracking method, comparing proposed algorithm with high performance method. We show propriety of proposed algorithm by means of the demonstrable study.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.561-566
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• 2002

This paper proposed trajectory tracking control of Mobile Robot. Trajectory tracking control scheme are Real coding Genetic-Algorithm and Back-propergation Algorithm. Control scheme ability experience proposed simulation.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.146-151
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• 2001

This paper proposed trajectory tracking control of Mobile Robot. Trajectory tracking control scheme are Real coding Genetic-Algorithm and Back-propergation Algorithm. Control scheme ability experience proposed simulation.