• 산업기술연구
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• 제20권B호
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• pp.189-194
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• 2000

In this paper, we present a BLDC Motor control for needle positioning and velocity control in the industrial sewing machine. In the industrial sewing machine, the fast acceleration control is needed, especially for a person who has a skill in operation of sewing machine for more products. And it is also needed to have a less noise and vibration. But the system which is made in a low price has no feedback system for a current control. Therefore we propose the method of velocity pattern that has an acceleration of velocity and Anti-windup algorithm. By the experiment, we confirmed that these manner have a good performance for low noise, low vibration and fast acceleration in the industrial sewing machine.

• 대한기계학회논문집A
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• 제26권2호
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• pp.243-253
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• 2002

This paper presents the system modeling, analysis, and controller design and implementation for a rotational inverted pendulum system(RIPS), which is an under-actuated system and has the problem of unattainable angular velocity state. A sliding mode controller using the parameterization of both the hyperplane and the compensator fur output feedback is applied to the RIPS. Also, to improve the performance of the control system, a disturbance observer which estimates the disturbance, parameter variation, and some modeling errors of RIPS with less computational effort is used together. The results of simulation and experiment show that the proposed control system has superior performance for disturbance rejection and regulation at certain initial conditions.

• 전자공학회논문지SC
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• 제43권1호
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• pp.13-20
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• 2006

본 논문에서는 로봇 말단부에 장착된 카메라 환경에서 시각 궤환을 이용하는 로봇 매니퓰레이터의 자세제어 기법을 제안한다. 자세제어를 위한 기준 관절 속도와 관절 가속도는 외부루프의 특징점을 이용한 영상정보에서 생성된다. 영상평면상에서 특징점을 추종하기 위한 제어 입력은 로봇 동역학을 사용하였다. 영상평면상의 특징점과 관절 각속도 오차를 로봇 구동을 위한 제어입력에 포함시켜 파라미터 불확실성에 강인하도록 하였다. 시스템의 안정도는 Lyapunov 안정도 판별법을 이용하여 입증하였다. 제안된 제어기의 성능은 5-링크 2 자유도를 가지는 로봇에 대한 컴퓨터 시뮬레이션과 실험을 통해 검증하였다.

• International Journal of Control, Automation, and Systems
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• 제6권3호
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• pp.386-393
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• 2008

A position control method for interpolating aspherical grinding and polishing tool path was reviewed and experimented in a nano precision machine. The position-base algorithm was reformed from the time-base algorithm, proposed in the previous study. The characteristics of the algorithm were in the velocity control loop with position feedback. The aspherical surface was divided by an interval at which each velocity and acceleration were calculated. The theoretical velocity was corrected by position error during processing. In the experiment, a machine was constructed and nano-scale linear encoders were installed at each axis. Relation between process parameters and the variation of position error was monitored and discussed. The best result from optimized parameters showed that the accuracy was 150nm and improved from the previous report.

• 한국정밀공학회지
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• 제10권4호
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• pp.54-63
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• 1993

This paper presents the dynamic modeling and control methodology to arrest structural deflections of industrial robotic manipulators featuring elastic members retrofitted with surface bonded pizoelectric actuators and sensors. The cynamic modeling is accomplished by employing a variational theorem, prior to developing a finite element formulation. This finite element formulation accounts for both original robot member elements and also bonded piezoelectric material elements. The governing equation of motion is then modified by condensing the electric potential vectors and subsequently two different negative velocity feedback controllers are established; a constant-gain feedback controller and a constant- amplitude feedback controller. By adopting a Model P50 articulating industrial robot manufactured by Gerneral Electric Company, conputer simulations are underlaken in order to demonstrate superior performance characteristics to be accrued from this proposed methodology such as smaller deflections at the end-effector.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.134-137
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• 2002

This paper presents a robot manipulator kinematic motion control scheme based on velocity feedback loop. The desired joint velocity is obtained by the feature-based visual servoing and is used in the joint velocity control loop system for trajectory control of the robot manipulator. The asymptotic stability of the closed loop system is shown by the Lyapunov method. Effectiveness of the proposed method is shown by simulation and experimental results on a robot manipulator with two degree of freedom.

• 한국생산제조학회지
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• 제24권2호
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• pp.141-147
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• 2015

In this study, a position control algorithm for an omni-directional mobile robot based on Mecanum wheels was introduced and experimentally evaluated. Multiple ultrasonic sensors were installed around the mobile robot to obtain position feedback. Using the distance of the robot from the wall, the position and orientation of the mobile robot were calculated. In accordance with the omni-directional velocity generation mechanism, the velocity kinematics between the Mecanum wheel and the mobile platform were determined. Based on this formulation, a simple and intuitive position control algorithm was suggested. To evaluate the control algorithm, a test bed composed of artificial walls was designed and implemented. While conventional control algorithms based on normal wheels require additional path planning for two-dimensional planar motion, the omni-directional mobile robot using distance sensors was able to directly follow target positions with the simple proposed position feedback algorithm.

• International Journal of Railway
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• 제4권4호
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• pp.93-96
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• 2011

The basic element of the EMS suspension is the electromagnet system, which suspends the vehicle without contact by attracting forces to the rails at the guideway. The suspension of a vehicle by attractive magnetic forces is inherently unstable and consequently it is continuously adjusted by the strength of the suspending electromagnet from rail irregularity and bending of the guideway. In order to improve reliable tracking, it needs to get feedback signals without measurement delay time. In this paper the concept of feedback control system with Kalman Filter in EMS is proposed. The input signals in the feedback control system are an air-gap and an acceleration signal. The air-gap signal with noise from the gap sensor is transformed to the filtered air-gap signal y without measurement delay time by using Kalman Filter. The filtered air-gap signal is transformed to a relative velocity and a relative acceleration signal. Then it multiplies these values by gain matrix in order to get the actuator's reference voltage value. The simulation results show that the dynamic responses of the suspension system can be improved by reducing the influence of measurement delay time of air-gap signals.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국내학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
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• pp.633-638
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• 1988

In this paper, a zero-velocity update technique to improve navigation accuracy of a SDINS(Strapdown Inertial Navigation System) has been studied. An indirect feedback Kalman filter which includes SDINS error equations based on a quaternion between body-fixed frame and local level navigation frame is employed for processing zero-velocity updates in an on-board navigation filter. Simulation results for land-mobile vehicle show that the zerovelocity update technique make a significant contribution to improving SDINS performance without any external aids.

• 대한전기학회논문지
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• 제39권12호
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• pp.1306-1316
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• 1990

A controller using a multilayer neural network is proposed to the dynamic control of a PUMA 560 robot arm. This controller is developed based on an error back-propagation (BP) neural network. Since the neural network can model an arbitrary nonlinear mapping, it is used as a commanded feedforward torque generator. A Proportional Derivative (PD) feedback controller is used in parallel with the feedforward neural network to train the system. The neural network was trained by the current state of the manipulator as well as the PD feedback error torque. No a priori knowledge on system dynamics is needed and this information is rather implicitly stored in the interconnection weights of the neural network. In another experiment, the neural network was trained with the current, past and future positions only without any use of velocity sensors. Form this thim window of position values, BP network implicitly filters out the velocity and acceleration components for each joint. Computer simulation demonstrates such powerful characteristics of the neurocontroller as adaptation to changing environments, robustness to sensor noise, and continuous performance improvement with self-learning.