• Journal of Aerospace System Engineering
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• v.11 no.5
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• pp.28-35
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• 2017

Magnetic Suspension and Balance System (MSBS) demonstrates the capacity to levitate an experimental model absent any mechanical contact using magnetic forces and moments. It allows precise control of position and attitude of the model, and measures external forces and moments acting on the model. For the purpose of acquisition of reliable experimental results under stable and safe conditions, the performance and robustness of the position and attitude control system of MSBS needs to be improved. To this end, Iterative Feedback Tuning (IFT) and L1 adaptive output feedback algorithm were employed to automatically increase command following performance and to ensure robust operation of MSBS with failure of electric power supply. The applicability was validated using computational simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.10
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• pp.1029-1038
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• 2009

This paper proposes a gain switching algorithm for joint position control of a hydraulic humanoid robot. Accurate position control of the lower body is one of the basic requirements for robust balance and walking control. Joint position control is more difficult for hydraulic robots than it is for electric robots because of an absence of reduction gear and better back-drivability of hydraulic joints. Backdrivability causes external forces and torques to have a large effect on the position of the joints. External ground reaction forces therefore prevent a simple proportional-derivative (PD) controller from realizing accurate and fast joint position control. We propose a state feedback controller for joint position control of the lower body, define three modes of state feedback gains, and switch the gains according to the Zero Moment Point (ZMP) and linear interpolation. Dynamic equations of hydraulic actuators were experimentally derived and applied to a robot simulator. Finally, the performance of the algorithm is evaluated with dynamic simulations.

• Proceedings of the KIEE Conference
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• 1988.11a
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• pp.28-31
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• 1988

This paper proposes a new linear adaptive position controller of DC servo motor. The proposed method can improve the drive performance and rapidly reject the state error caused by both parameter variations and force disturbance. The structure of this adaptive control method is based multiloop feedback control and model reference control. Simulation results are presented to verify the improved response when parameter variations and load disturbance give relatively significant effects to the servo system.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.566-569
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• 1987

The primary difficulties encountered in the use of step motors are underdamped response when stopping at a specified position and dynamic instability during high-speed slewing. This paper proposes a speed and position detection scheme using the back EMF generated by the rotating permanent magnet field of a two-phase 1.8.deg. hybrid step motor, and presents its application to the state-variable feedback control of the hybrid step motor. All simulation results in a single step response show that the hybrid step motor performances such as peak overshoot and settling time are greatly improved.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.05a
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• pp.109-109
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• 2004

구조물에 있어서 진동은 소음을 유발하며 구조물을 불안정하게 만드는 요인이 된다. 특히, 저주파 진동은 구조물에 미치는 영향이 크므로, 유연구조물의 저주파 진동모드들을 우선적으로 제어할 필요가 있다. 본 논문에서는 압전세라믹을 작동기로, 압전필름을 감지기로 사용하였고, 능동 제어 알고리즘의 하나인 적응형 디지털 PPF(Positive Position Feedback) 제어기를 구성하여 유연 외팔 평판과 열린 박스 구조물에서 저주파 진동을 감소시켰다.(중략)

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.7
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• pp.1307-1313
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• 1992

The end-point position control of a flexible manipulator is a non-minimum phase system. The PD feedback of the end-point position is not stable in contrast with that of the hub jangle. However, the system can be stabilized conditionally by the feedback of both the hub rate angle and the end-point position. Even in the non-minimum system, the LQG/LTR control law is more systmatic controller design method than the classical control law which uses a root-locus technique.

• Journal of Power System Engineering
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• v.10 no.3
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• pp.97-103
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• 2006

In this study, a position trajectory tracking control algorithm is proposed for a pneumatic artificial muscle driving apparatus composed of a actuator which imitates the muscle of human, a position sensor and a control valve. The controller applied to the driving apparatus is composed of a state feedback controller and disturbance observer. The feedback controller which feeds back position, velocity and acceleration is derived from the linear model of pneumatic artificial muscle driving apparatus. The disturbance observer is designed to improve trajectory tracking performance and to reduce the effect of model discrepancy. The effectiveness of the designed controller is proved by experiments and the experimental results show that the pneumatic artificial muscle driving apparatus with the proposed control algorithm tracks given position reference inputs accurately.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.9
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• pp.1245-1247
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• 2014

This paper presents a robust position tracking controller for motor-driven flexible joint manipulators using only the motor angle measurement. The control problem is not easy because the link position is hard to estimate in the presence of parameter uncertainties. The proposed controller consists of a feedback linearization controller (FLC) and two proportional-integral observers (PIOs) that estimate both system states including the link position and an equivalent disturbance for compensating the parameter uncertainties. Comparative computer simulations are conducted to demonstrate the effectiveness of the proposed control algorithm.

• Journal of Advanced Marine Engineering and Technology
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• v.21 no.3
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• pp.284-292
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• 1997

This paper is concerned with position control of direct drive robots. The proposed algorithm consists of the feedback controller and neural networks. Mter the completion of learning, the output of the feedback controller is nearly equal to zero, and the neural networks play an important role in the control system. Therefore, the optimum retuning of control parameters is unnecessary. In other words, the proposed algorithm does not need any knowledge of the con¬trolled system in advance. The effectiveness of the proposed algorithm is demonstrated by the experiment on the position control of a parallelogram link-type direct drive robot.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.460-463
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• 1995

The present paper studies a robot manipulator's contact tasks on the uncertain flexible objects. The flexible object's distributed parameter model is approximated into a lumped "position state-varying" model. By using the well-known nonlinear feedback compensation, the robot's control space is decomposed into the position control subspace and the object's torque control subspace. The optimal state feedback is designed for the position loop, and the robot's contact force is controlled through controlling the resultant torque on the object using model-reference simple adaptive control. Experiments of a PUMA robot interacting with an aluminum plate show the effectiveness of this control approach. approach.