• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2004년도 추계학술대회
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• pp.234-239
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• 2004

This paper describes preliminary results we have obtained in developing a computer vision system based on active IR illumination for real time gaze tracking for interactive graphic display. Unlike most of the existing gaze tracking techniques, which often require assuming a static head to work well and require a cumbersome calibration process fur each person, our gaze tracker can perform robust and accurate gaze estimation without calibration and under rather significant head movement. This is made possible by a new gaze calibration procedure that identifies the mapping from pupil parameters to screen coordinates using the Generalized Regression Neural Networks (GRNN). With GRNN, the mapping does not have to be an analytical function and head movement is explicitly accounted for by the gaze mapping function. Furthermore, the mapping function can generalize to other individuals not used in the training. The effectiveness of our gaze tracker is demonstrated by preliminary experiments that involve gaze-contingent interactive graphic display.

• 한국정밀공학회지
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• 제21권11호
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• pp.83-90
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• 2004

In this paper we present a new real-time visual servoing unit for laparoscopic surgery This unit can automatically control laparoscope manipulator through visual tracking of laparoscopic surgical tool. For the tracking, we present two-stage adaptive CONDENSATION(conditional density propagation) algorithm to extract the accurate position of the surgical tool tip from a surgical image sequence in real-time. This algorithm can be adaptable to abrupt change of laparoscope illumination. For the control, we present virtual damper system to control a laparoscope manipulator safely and stably. This system causes the laparoscope to move under constraint of the virtual dampers which are linked to the four sides of image. The visual servoing unit operates the manipulator in real-time with locating the surgical tool in the center of image. The experimental results show that the proposed visual tracking algorithm is highly robust and the controlled manipulator can present stable view with safe.

• 반도체디스플레이기술학회지
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• 제22권3호
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• pp.161-165
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• 2023

One of the difficult works in an autonomous driving system is detecting road lanes or objects in the road boundaries. Detecting and tracking a vehicle is able to play an important role on providing important information in the framework of advanced driver assistance systems such as identifying road traffic conditions and crime situations. This paper proposes a vehicle detection scheme based on deep learning to classify and tracking vehicles in a complex and diverse environment. We use the modified YOLO as the object detector and polynomial regression as object tracker in the driving video. With the experimental results, using YOLO model as deep learning model, it is possible to quickly and accurately perform robust vehicle tracking in various environments, compared to the traditional method.

• 제어로봇시스템학회논문지
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• 제10권12호
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• pp.1249-1255
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• 2004

A new parameter adaptation scheme for RBF Neural Network (NN) has been developed in this paper. Even though the RBF Neural Network (NN) based controllers are robust against both un-modeled dynamics and external disturbances, the performance is not satisfactory for a fast and precise mobile robot. To improve the tracking performance as well as robustness, all the parameters of RBF NN are updated in real time. The stability of this control law is rigorously proved by following the Lyapunov stability theory and shown by the experimental simulations. The fact that all of the weighting factors, width and center of RBF NN have been updated implies that this scheme utilizes all the possibilities in RBF NN to make the controller robust and precise while the mobile robot is following un-known trajectories. The performance of this new algorithm has been compared to the conventional RBF NN controller where some of the parameters are adjusted for robustness.

• International Journal of Control, Automation, and Systems
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• 제6권5호
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• pp.702-712
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• 2008

This paper presents a novel approach for controlling electrically driven robot manipulators based on voltage control. The voltage-based control is preferred comparing to torque-based control. This approach is robust in the presence of manipulator uncertainties since it is free of the manipulator model. The control law is very simple, fast response, efficient, robust, and can be used for high-speed tracking purposes. The feedback linearization is applied on the electrical equations of the dc motors to cancel the current terms which transfer all manipulator dynamics to the electrical circuit of motor. The control system is simulated for position control of the PUMA 560 robot driven by permanent magnet dc motors.

• 한국지능시스템학회논문지
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• 제8권4호
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• pp.62-68
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• 1998

본 연구에서는 유도모터 드라이브의 파라미터변동 및 외란의 효과를 고려한 속도 제어기를 설계하였다. 제안된 퍼지 P-I 제어기는 원하는 속도 추종사양에 합치되도록 설계되었으며 외란에 기인된 파라미터 변동의 영향을 최소화 하기위해 퍼지 가중행력을 이용한 견실제어기를 구성하였다. 드라이브의 원하는 속도 추종 제어성은 넓은 동작 범위내에서 지속되었으며 양호한 속도 성능을 모의 실험에 의해 확인 하였다.

• 한국정밀공학회지
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• 제29권1호
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• pp.79-86
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• 2012

This paper proposes PID and RIC (Robust Internal-loop Compensator) based motion controller using dual learning algorithm for position control of linear synchronous motor respectively. Its gains are auto-tuned by using two learning algorithms, reinforcement learning and neural network. The feedback controller gains are tuned by reinforcement learning, and then the feedforward controller gains are tuned by neural network. Experiments prove the validity of dual learning algorithm. The RIC controller has better performance than does the PID-feedforward controller in reducing tracking error and disturbance rejection. Neural network shows its ability to decrease tracking error and to reject disturbance in the stop range of the target position and home.

• 한국정밀공학회지
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• 제23권1호
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• pp.121-128
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• 2006

This paper deals with the application of enhanced perturbation estimation (SMCEPE) to sliding mode control of a dynamic system in the presence of perturbations including external disturbances, unpredictable parameter variations, and unstructured dynamics. Compared to conventional sliding mode control (SMC) and sliding mode control with perturbation estimation (SMCPE), the proposed one can offer robust control performances under serious control conditions, such as fast dynamic perturbations and slow loop-closure speeds, without a priori knowledge on upper bounds of perturbations. The perturbation estimator in SHCEPE also has more adaptability owing to the fractional-order hold technique. The effectiveness and superiority of the proposed control strategy are demonstrated by a series of simulations on the position tracking control of a two-link robot manipulator.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 A
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• pp.187-190
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• 2003

In this paper, we address the robust adaptive backstepping controller using fuzzy neural network (FHIN) for a class of uncertain output feedback nonlinear systems with disturbance. A new algorithm is proposed for estimation of unknown bounds and adaptive control of the uncertain nonlinear systems. The state estimation is solved using K-fillers. All unknown nonlinear functions are approximated by FNN. The FNN weight adaptation rule is derived from Lyapunov stability analysis and guarantees that the adapted weight error and tracking error are bounded. The compensated controller is designed to compensate the FNN approximation error and external disturbance. Finally, simulation results show that the proposed controller can achieve favorable tracking performance and robustness with regard to unknown function and external disturbance.

• International Journal of Precision Engineering and Manufacturing
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• 제3권4호
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• pp.78-86
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• 2002

In this paper, LMI (Linear Matrix Inequality) based on H$\_$$\infty$/ controller for a lire of sight (LOS) stabilization system. It shows that the proposed controller has more excellent stabilization performance than that of the conventional PI-Lead controller. An H$\_$$\infty$/ control has been also applied to the system for reducing modeling errors and the settling time of the system. The LMI-based H$\_$$\infty$/ controller design is more practical in view of reducing a run-time than Riccati-based H$\_$$\infty$/ controller. This H$\_$$\infty$/ controller is available not only to decrease the gain in PI-Lead control, but also to compensate the identifications for the various uncertain parameters. Therefore, this paper, shows that the proposed LMI-based H$\_$$\infty$/ controller had good disturbance attenuation and reference input tracking performance compared with the control performance of the conventional controller under any real disturbances.