• Journal of computational fluids engineering
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• v.20 no.2
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• pp.81-87
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• 2015

For Underwater vehicles, Unwanted roll excursions are inevitable as they are caused by induced propeller torque, disturbances, and banking motion during turns. To estimate the manoeuvring performance of underwater vehicle, it is necessary to obtain the roll coefficient of body. This paper was covered estimation of roll coefficient of underwater vehicle using STAR-CCM+, commercial CFD(Computational Fluid Dynamics) code. The RANS equations for incompressible fluid flows was solved numerically by using a finite volume method. An MRF(Moving Reference Frame) Method was Also adopted for rotations of body. For the validation, the flow around a DARPA SUBOFF bare hull model was simulated and good agreement with experiments was obtained. And Pure roll coefficients were calculated and campared with the experimental data which were presented by Seoul National University. Finally, an underwater vehicle model with propeller was simulated and analyzed for estimation of roll coefficient variation caused by induced propeller torque.

• 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.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.439-442
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• 1999

In this paper presents a modeling method for simulation of Tration Motor using Matlab/Simulink This model is well adopted in the traction motor and speed, torque estimation. This model consist o IM(induction motor) block, Load block and controller blocks. This paper use the indirect vecto control Because improve accuracy speed characters. The result show the better speed and torque characters. And effectiveness of Matlab/Simulink in simulations.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.2
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• pp.73-79
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• 1999

Most of mechanical structures are combined of substructures such as beams and/or plates. There are few systems with unibody structures but are many systems with united body structures. Generally the dynamic a nalysis of whole structures is performed under alternation load. In the structure design, the analysis of each bolted joint is more important than others for zero severity. This paper presents the analysis method of contact stress distribution factor in the bolted joint with variable fastening torque on joints in the structure. At first, a static vibration test was performed to find out a nominal stress of bolt jointed plates from the relationship between natural frequency and nominal stress. Then a contact stress was computed at contact point between bolt and plate in the structure. It is believed that the proposed method has promisiong implications for safer design with index of contact stress distribution factor and has merits for cost-down and saving time at the beginning of vehicle development.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.739-744
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• 2004

This paper presents an adaptive approach to control the amount of slip of the torque converter bypass clutch using its estimated friction coefficient. The proposed approach can be readily implemented using the inexpensive speed sensors currently installed in an automobile. A measurement feedback control law to drive the slip error to zero together with an adaptation law to identify the unknown friction coefficient is developed using the Lyapunov control design method. The robustness of the control and adaptation laws to parametric and/or torque uncertainties as well as the convergence of the friction coefficient are investigated. Simulation results verify the viability of the proposed control algorithm in real-world vehicle control applications.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.3B no.1
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• pp.59-64
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• 2003

This paper presents a new rotor position estimation method for brushless DC motors. The estimation error of the rotor position clearly provokes the phase shift angle misaligned between the phase current and the back-EMF waveforms, which causes torque ripple in brushless DC motor drives. Such an estimation error can be reduced with the help of the proposed neutral-voltage-based estimation method, which is structured as a closed loop observer. A neutral voltage appearing during the normal mode of the inverter operation is found to be an observable and control table measure, which can be used for estimating an exact rotor position. This neutral voltage is obtained from the DC-link current, the switching logic, and the motor speed values. The proposed algorithm, which can be easily implemented by using a single DC-link current and the motor terminal voltage sensors, is verified by simulation and experiment results.

• Journal of Power Electronics
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• v.5 no.4
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• pp.282-288
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• 2005

In this paper, a new speed sensorless induction motor scheme which can estimate rotor speed and rotor resistance simultaneously is presented. The rotor flux with a low frequency sinusoidal waveform is used to conduct on-line simultaneous estimation of the rotor speed and rotor resistance. Hence the proposed sensorless control method is robust to rotor resistance variations. Also, the control scheme has no current minor loop to determine voltage references. It contributes to good control performance at low speeds. Some simulation results supported by experiments are given to show the effectiveness of this method.

• Journal of Biomedical Engineering Research
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• v.22 no.3
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• pp.293-301
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• 2001

A patient-specific musculoskeletal model, whose parameters can be identified noninvasively, was developed for the automatic generation of patient-specific stimulation pattern in FES. The musculotendon system was modeled as a torque-generator and all the passive systems of the musculotendon working at the same joint were included in the skeletal model. Through this, it became possible that the whole model to be identified by using the experimental joint torque or the joint angle trajectories. The model parameters were grouped as recruitment of muscle fibers, passive skeletal system, static and dynamic musculotendon systems, which were identified later in sequence. The parameters in each group were successfully estimated and the maximum normalized RMS errors in all the estimation process was 8%. The model predictions with estimated parameter values were in a good agreement with the experimental results for the sinusoidal, triangular and sawlike stimulation, where the normalized RMS error was less than 17%, Above results show that the suggested musculoskeletal model and its parameter estimation method is reliable.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.5
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• pp.32-43
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• 2007

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current and voltage rated value. This paper proposes speed control of IPMSM using adaptive learning fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive learning fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive learning fuzzy neural network and artificial neural network.

• The Transactions of the Korean Institute of Power Electronics
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• v.7 no.6
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• pp.509-516
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• 2002

This paper presents a simple torque estimation method and switching angle control of Switched Reluctance Motor(SRM) using Neural Network(NN). SRM has gaining much interest as industrial applications due to the simple structure and high efficiency. Adaptive switching angle control is essential for the optimal driving of SRM because of the driving characteristic varies with the load and speed. The proper switching angle which can increase the efficiency was investigated in this paper. NN was adapted to regulate the switching angle and nonlinear inductance modelling. Experimental result shows the validity of the switching angle controller.