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A Novel Visual Servoing Approach For Keeping Feature Points Within The Field-at-View
박도환(Do-Hwan Park),염준형(Joon-Hyung Yeom),박노용(Noh-Yong Park),하인중(In-Joong Ha) 대한전기학회 2007 대한전기학회 학술대회 논문집 Vol.2007 No.4
In this paper, an eye-in-hand visual servoing strategy for keeping feature points within the FOV(field-of-view) is proposed. We first specify the FOV constraint which must be satisfied to keep the feature points within the FOV. It is expressed as the inequality relationship between (Ⅰ) the LOS(line-of-sight) angles of the center of the feature points from the optical axis of the camera and (Ⅱ) the distance between the object and the camera. We then design a nonlinear feedback controller which decouples linearly the translational and rotational control loops. Finally, we show that appropriate choice of the controller gains assures to satisfy the FOV constraint. The main advantage of our approach over the previous ones is that the trajectory of the camera is smooth and circular-like. Furthermore, ours can be applied to the large camera displacement problem.
주기적인 외란 제거에 있어서 빠른 오프라인 학습 제어 접근 방식
하인중(In-Joong Ha),장정국(Jung-Kook Jang),박진원(Jin-Won Park),권정훈(Jung-Hoon Kwon) 대한전기학회 2007 대한전기학회 학술대회 논문집 Vol.2007 No.4
The recently-developed off-line learning control approaches for the rejection of periodic disturbances utilize the specific property that the learning system tends to oscillate in steady state. Unfortunately, the prior works have not clarified how closely the learning system should approach the steady state to achieve the rejection of periodic disturbances to satisfactory level. In this paper, we address this issue extensively for the class of linear systems. We also attempt to remove the effect of other aperiodic disturbances on the rejection of the periodic disturbances effectively. In fact, the proposed learning control algorithm can provide very fast convergence performance in the presence of aperiodic disturbance. The effectiveness and practicality of our work is demonstrated through mathematical performance analysis as well as various simulation results.
김창환(Chang-Hwan Kim),하인중(In-Joong Ha),하태균(Tae-Kyoon Ha),고명삼(Myoung-Sam Ko),김동일(Dong-Il Kim) 대한전자공학회 1992 대한전자공학회 학술대회 Vol.1992 No.10
In this paper, we present a DSP-based high dynamic performance torque control scheme of variable reluctance motors(VRM's) for DD(Direct Drive) robots via function inversion technique. The VRM with our controller behaves like DC motors, and hence developed torque tracks given torque command accurately with no torque ripples. Furthermore, our torque control algorithm ensures the production of maximum constant torque under maximum current limitation, minimizes power loss in each phase resistance, and takes magnetic saturation effect into account. Also, since our control algorithm is represented in the form of look-up table, it can be easily implemented with simple digital circuits and this tabular design method is computationally more accurate and simpler compared to the prior methods.
Automatic Error Correction of Position Sensors for Servo Motors via Iterative Learning
한석희,하태균,허헌,하인중,고명삼,Han, Seok-Hee,Ha, Tae-Kyoon,Huh, Heon,Ha, In-Joong,Ko, Myoung-Sam The Institute of Electronics and Information Engin 1994 전자공학회논문지-B Vol.b31 No.9
In this paper, we present an iterative learning method of compensating for position sensor error. The previously known compensation algorithms need a special perfect position sensor or a priori information about error sources, while ours does not. to our best knowledge, any iterative learning approach has not been taken for sensor error compensation. Furthermore, our iterativelearning algorithm does not have the drawbacks of the existing interativelearning control theories. To be more specivic, our algorithm learns an uncertain function itself rather than its special time-trajectory and does not reuquest the derivatives of measurement signals. Moreover, it does not require the learning system to start with the same initial condition for all iterations. To illuminate the generality and practical use of our algorithm, we give the rigorous proof for its convergence and some experimental results.