• Journal of the Korean Society of Manufacturing Process Engineers
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• v.6 no.2
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• pp.41-46
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• 2007

Butterfly valves are commonly used as control valves in applications where the pressure drops required of the valves relatively low. As the shutoff valve (on/off service) or throttling valves (for flow or pressure control), the higher order and the better precision of butterfly valves are required. The it's more and more essential to know the flow characteristic around the valve. Due to the fast progress of the flow visualization and numerical technique, it becomes possible to observe the flows around a valve and to estimate the performance of a valve. Researching these results did not gave only access to understand the process of the valve flows at different valve opening angles, but also was made to determine the accuracy of the employed method. Furthermore, the results of the three-dimensional analysis can be used in the design of butterfly valve in the industry.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.5
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• pp.332-337
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• 2004

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying. parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• Journal of information and communication convergence engineering
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• v.6 no.4
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• pp.421-424
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• 2008

In torque transmission system, we must suppressed vibration for Accuracy characteristic response of motor, Therefore, vibration suppression factor is very important motor control. To suppress vibration, a various control method has been proposed. Specially, one method of vibration suppression used disturbance observer filter. This method is torsion torque passing disturbance observer filter. By the estimated torsion torque feedback, vibration can be suppressed. The CDM(coefficient diagram method) is used to design the filter and Proportional controller. But using coefficient diagram method, not adapted controller parameter in disturbance. For this solution, we used fuzzy controller for auto tuning controller parameter. We proved this approach is confirmed by simulation.

• The Journal of Korea Robotics Society
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• v.15 no.2
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• pp.169-176
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• 2020

This paper studied the collision detection of robot manipulators for safe collaboration in human-robot interaction. Based on sensor-based collision detection, external torque is detached from subtracting robot dynamics. To detect collision using joint torque sensor data, a comparative study was conducted using data-based machine learning algorithm. Data was collected from the actual 3 degree-of-freedom (DOF) robot manipulator, and the data was labeled by threshold and handwork. Using support vector machine (SVM), decision tree and k-nearest neighbors KNN method, we derive the optimal parameters of each algorithm and compare the collision classification performance. The simulation results are analyzed for each method, and we confirmed that by an optimal collision status detection model with high prediction accuracy.

• The Journal of Korea Robotics Society
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• v.15 no.1
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• pp.77-89
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• 2020

This paper proposes a simple and intuitive model-free torque-tracking control for rotary electro-hydraulic actuators. The undesirable natural-velocity-feedback effect is discussed by introducing mechanical impedance into the electro-hydraulic actuation system. The proposed model-free torque control comprises inner- and outer-loop control to achieve two control objectives. Inner-loop control reduces the mechanical impedance passively and optimally. To improve the tracking accuracy, a certain form of proportional-integral-derivative control is applied to the outer loop. The robustness of the proposed closed-loop system against external disturbances is demonstrated by transforming the two-loop control structure into a disturbance observer form. The proposed method is validated on a single joint electro-hydraulic actuator.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.429-433
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• 2007

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and ststor resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• Journal of Ocean Engineering and Technology
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• v.29 no.5
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• pp.398-403
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• 2015

This paper introduces a method to estimate the contact-force of the leg of a walking robot and proposes a solution to a shortcoming of the previous study. This shortcoming was the deteriorating performance when estimating the contact-force whenever the rotation of each joint was reversed. It occurred because the friction-torque of each joint was not considered. In order to solve this problem, a friction-torque model for a robot leg was developed based on repetitive experimentation and used to improve the contact-force estimation performance. We verified the performance of the proposed method experimentally.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.207-209
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• 2006

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and ststor resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• JSTS:Journal of Semiconductor Technology and Science
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• v.13 no.1
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• pp.28-33
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• 2013

Spin-torque oscillators (STO) is a new device that can be used as a tunable microwave source in various wireless devices. Spin-transfer torque effect in magnetic multilayered nanostructure can induce precession of magnetization when bias current and external magnetic field are properly applied, and a microwave signal is generated from that precession. We proposed a semi-empirical circuit-level model of an STO in previous work. In this paper, we present a refined STO model which gives more accuracy by considering physical phenomena in the calculation of effective field. Characteristics of the STO are expressed as functions of external magnetic field and bias current in Verilog-A HDL such that they can be simulated with circuit-level simulators such as Hspice. The simulation results are in good agreement with the experimental data.

• The Transactions of the Korean Institute of Power Electronics
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• v.21 no.1
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• pp.65-71
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• 2016

This study investigates a high-accuracy alternate QD model to estimate the characteristics of induction motor under light loads. To demonstrate the usefulness of the alternate QD model, a maximum torque per amp (MTPA) control based on the alternate model is shown to outperform MTPA control based on the standard QD model. The experimental study conducted in this work exhibits that the MTPA control based on the alternate QD model tracks torque commands between 20 Nm and 30 Nm with 5% error, whereas the MTPA control based on the standard QD model generates torques lower by over 23% compared with the aforementioned torque commands. This result indicates that the alternate QD model is a highly accurate model for induction motors under light loads.