• The Transactions of the Korean Institute of Power Electronics
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• v.22 no.1
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• pp.53-59
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• 2017

The information on the actual voltages and actual currents of the motor is required for the sensorless control of a permanent magnet synchronous motor without rotor position sensors. In the model-based rotor position estimator of a PM synchronous motor, the reference voltages, which are the outputs of the current controller, are commonly used. The reference voltages in over-modulation regions for high-speed operation differ from the actual voltages applied to the motor. Consequently, the estimated rotor position and rotor speed may fail to track the real rotor position and real rotor speed. In this paper, the sensorless control for a PM synchronous motor in over-modulation regions for high-speed operation is proposed. The three-phase voltages applied to the motor are measured by using additional voltage detection circuits, and the performance of the rotor position estimator based on the measured three-phase voltages is validated through the experimental results.

• Journal of Electrical Engineering and Technology
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• v.11 no.1
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• pp.29-37
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• 2016

Fast and accurate fault diagnosis of the position sensor is of great significance to ensure the reliability as well as sensor fault tolerant operation of the Switched Reluctance Wind Generator (SRWG). This paper presents a fault diagnostic scheme for a SRWG based on the residual between the estimated rotor position and the actual output of the position sensor. Extreme Learning Machine (ELM), which could build a nonlinear mapping among flux linkage, current and rotor position, is utilized to design an assembled estimator for the rotor position detection. The data for building the ELM based assembled position estimator is derived from the magnetization curves which are obtained from Finite Element Analysis (FEA) of an SRWG with the structure of 8 stator poles and 6 rotor poles. The effectiveness and accuracy of the proposed fault diagnosis method are verified by simulation at various operating conditions. The results provide a feasible theoretical and technical basis for the effective condition monitoring and predictive maintenance of SRWG.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.792-795
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• 1996

In TIG welding of pipe, back bead size monitoring is important for weld quality assurance. Many researches have been performed on estimation of the back bead size by heat conduction analysis. However numerical conduction model based on many uncertain thermal parameters causes remarkable errors and thermomechanical phenomena in molten pool can not be considered. In this paper, filler wire feeding force in addition to weld current, wire feedrate, torch travel speed and orbital position angle is monitored to estimate back bead size in orbital TIG welding. Monitored welding process variables are fed into an artificial neural network estimator which has been trained with the monitored process variables (input patterns) and actual back bead size (output patterns). Experimental verification of the proposed estimation method was performed. The predicted results are in a good agreement with the actual back bead shape. The results are quite promising in that estimation of invisible back bead shape can be achieved by analyzing the welding parameters without any conventional NDT of welds.

• JSTS:Journal of Semiconductor Technology and Science
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• v.13 no.5
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• pp.430-442
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• 2013

This paper presents a fast multi-reference frame integer motion estimator for H.264/AVC. The proposed system uses the previously proposed fast multi-reference frame algorithm. The previously proposed algorithm executes a full search area motion estimation in reference frames 0 and 1. After that, the search areas of motion estimation in reference frames 2, 3 and 4 are minimized by a linear relationship between the motion vector and the distances from the current frame to the reference frames. For hardware implementation, the modified algorithm optimizes the search area, reduces the overlapping search area and modifies a division equation. Because the search area is reduced, the amount of computation is reduced by 58.7%. In experimental results, the modified algorithm shows an increase of bit-rate in 0.36% when compared with the five reference frame standard. The pipeline structure and the memory controller are also adopted for real-time video encoding. The proposed system is implemented using 0.13 um CMOS technology, and the gate count is 1089K with 6.50 KB of internal SRAM. It can encode a Full HD video ($1920{\times}1080P@30Hz$) in real-time at a 135 MHz clock speed with 5 reference frames.

• Proceedings of the KIEE Conference
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• 1998.07a
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• pp.260-262
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• 1998

A new method of induction motor drive, which requires not shaft encoder, is presented. This system has both torque and speed controls that are performed by vector control. The scheme is on the basis of a rotor flux speed control, which is performed by torque producing current and rotor flux, derived from the stator voltages and currents. But, there is a problem with respect to the calculated rotor flux vector, which is an integrating operation by which the rotor induced voltage is converted into the rotor flux. The calculated rotor flux does not work so that it is unstable in initial operation, as motor speed approaches zero. For the proposed rotor flux estimator, a lag circuit is employed, to which both the motor-induced voltage and rotor flux command are imposed, and it is possible to calculate even a low frequency down to standstill. We show the validity of the proposed control method through several computer simulations.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.1
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• pp.18-27
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• 2002

This paper presents an implementation of digital high-performance speed sensorless control system of an induction motor drives with Direct Torque Control(DTC). The system consists of closed loop stator flux and torque observer, speed and torque estimators, two hysteresis controllers, an optimal switching look-up table, IGBT voltage source inverter, and TMS320C31 DSP controller board. The stator flux observer is based on the combined current and voltage model with stator flux feedback adaptive control for wide speed range. The speed estimator is using the model reference adaptive system(MRAS) with rotor flux linkages for speed turning signal estimation. In order to prove the suggested speed sensorless control algorithm, and to obtain a high-dynamic robust adaptive performance, we have some simulations and actual experiments at low(20rpm) and high(1000rpm) speed areas. The developed speed sensorless system are shown a good speed control response characteristic, and high performance features using 2.2[kW] general purposed induction motor.

• The Transactions of the Korean Institute of Power Electronics
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• v.10 no.2
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• pp.186-193
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• 2005

This paper presents a strategy to drive built in-type spindle induction motor which is used as CNC(Computer Numerical Control) in the industry. Gopinath model flux estimator which is composed of current model to be profitable in the low speed range and voltage model to be profitable in the high speed range is used for rotor flux estimation. Moreover this paper presents to drive the spindle motor in the high speed range by using the flux weakening control. High speed operation of spindle motor in the field weakening region is verified through simulations and experiments.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.550-558
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• 2014

Direct Torque controlled induction motor (DTC-IM) drives use stator resistance of the motor for stator flux estimation. So, stator resistance estimation properly is very important for a stable and effective operation of the induction motor. Stator resistance variations because of changing in temperature make DTC operation difficult mainly at low speed. A method based on artificial neural network (ANN) to estimate the stator resistance online of IM for DTC drive is modeled and verified in this paper. To train the neural network a back propagation algorithm is used. Weight adjustment of neural network is done by back propagating the error signal between measured and estimated stator current. An extensive simulation has been carried out in MATLAB/SIMULINK to prove the efficacy of the proposed stator resistance estimator. The simulation & experimental result reveals that proposed method is able to obtain precise torque and flux control at low speed.

• Nuclear Engineering and Technology
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• v.51 no.4
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• pp.968-976
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• 2019

This paper presents a number of verification case studies for a recently developed sensitivity/uncertainty code package. The code package, ROMUSE (Reduced Order Modeling based Uncertainty/Sensitivity Estimator) is an effort to provide an analysis tool to be used in conjunction with reactor core simulators, in particular the Virtual Environment for Reactor Applications (VERA) core simulator. ROMUSE has been written in C++ and is currently capable of performing various types of parameter perturbations and associated sensitivity analysis, uncertainty quantification, surrogate model construction and subspace analysis. The current version 2.0 has the capability to interface with the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) code, which gives ROMUSE access to the various algorithms implemented within DAKOTA, most importantly model calibration. The verification study is performed via two basic problems and two reactor physics models. The first problem is used to verify the ROMUSE single physics gradient-based range finding algorithm capability using an abstract quadratic model. The second problem is the Brusselator problem, which is a coupled problem representative of multi-physics problems. This problem is used to test the capability of constructing surrogates via ROMUSE-DAKOTA. Finally, light water reactor pin cell and sodium-cooled fast reactor fuel assembly problems are simulated via SCALE 6.1 to test ROMUSE capability for uncertainty quantification and sensitivity analysis purposes.

• The Journal of Korea Robotics Society
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• v.18 no.1
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• pp.72-81
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• 2023

We introduce tightly-coupled GNSS-LiDAR-Inertial state estimator, which is capable of SLAM (Simultaneously Localization and Mapping) and autonomous driving. Long term drift is one of the main sources of estimation error, and some LiDAR SLAM framework utilize loop closure to overcome this error. However, when loop closing event happens, one's current state could change abruptly and pose some safety issues on drivers. Directly utilizing GNSS (Global Navigation Satellite System) positioning information could help alleviating this problem, but accurate information is not always available and inaccurate vertical positioning issues still exist. We thus propose our method which tightly couples raw GNSS measurements into LiDAR-Inertial SLAM framework which can handle satellite positioning information regardless of its uncertainty. Also, with NLOS (Non-light-of-sight) satellite signal handling, we can estimate our states more smoothly and accurately. With several autonomous driving tests on AGV (Autonomous Ground Vehicle), we verified that our method can be applied to real-world problem.