• Journal of Advanced Marine Engineering and Technology
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• v.17 no.2
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• pp.29-35
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• 1993

Recent marine propulsion diesel engines tend to become slower in speed and longer in stroke for the higher engine efficiency, and in these long stroke and slow speed engines the digital governors are highly recommended to be used. But, in the present digital governors only the feedback of the engine rpm-signal is used for the engine speed control. If the load torque of the engine can be measured or estimated and the torque feedback loop is added to the present digital governor, it is expected that the speed control performance of the digital governor will be highly improved. In this paper, a new method is proposed to estimate the load torque of the diesel engine from the measured signals of fuel oil and rpm. And it is also suggested that the Kalman filter can be used for the estimation of engine torque.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.2
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• pp.73-79
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• 2005

This paper is proposed hybrid artificial intelligent (HAI) controller based on the vector controlled induction motor drive system. The hybrid combination of fuzzy control and neural network will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed estimation of induction motor using a closed-loop state observer. The rotor position is calculated through the stator flux position and an estimated flux value of rotation reference frame. A closed-loop state observer is implemented to compute the speed feedback signal. The results of analysis prove that the proposed control system has strong robustness to rotor parameter variation, and has good steady-state accuracy and transitory response.

• Journal of Advanced Marine Engineering and Technology
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• v.31 no.4
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• pp.441-447
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• 2007

The speed and torque control performance of the DC motor used in the industrial field is affected by the change of parameters. In particular the change of armature resistance can be greatly originated from various load conditions. Accordingly a large number of studies to estimate those problems in the servo motor control field have already been under way. In this paper, the armature resistance on the DC motor under several load and speed conditions is estimated by the Artificial Neural Network(ANN), and the validity is proven by the speed estimation of sensorless speed control system.

• Proceedings of the KIEE Conference
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• summer
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• pp.1245-1247
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• 2004

This paper considers the design and implementation of novel technique of speed estimation and control for IPMSM using hybrid intelligent control. The hybrid combination of neural network and adaptive fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of IPMSM using adaptive neural network fuzzy(A-NNF) and estimation of speed using artificial neural network(ANN) controller. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new hybrid intelligent control.

• The Transactions of the Korean Institute of Power Electronics
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• v.24 no.2
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• pp.127-133
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• 2019

This paper proposes a restarting method for extended electromotive force (EEMF)-based sensorless permanent magnet synchronous motor (PMSM) drive systems. The sensorless PMSM drive systems generally estimate the rotor speed and angle based on EEMF. However, if the inverter is stopped while the PMSM is rotating, the initial rotor speed and angle are required for restart. Therefore, the proposed restarting method estimates the initial rotor speed and angle using the short-circuit current generated by applying zero voltage vector from the inverter. The validity of the proposed method is verified by simulation and experimental results.

• Journal of the Korean Society of Safety
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• v.28 no.1
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• pp.63-73
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• 2013

Dynamic strain history curve measured in the field is influenced by various factors such as vehicle type, speed, noise, temperature and running location etc.. Because such curve is used for vehicle weight estimation methodology suggested by Moses, exact strain history curve is the most important thing for exact estimation of vehicle weight. In this paper, effect of such factors mentioned above is investigated on the measured strain history curves, and results of weight estimation of vehicles are discussed quantitatively. From this study, it was known that temperature effect contained in the strain history curve measured for long time in-site gives the biggest effect on result of weight estimation and it can be removed by using the mode value. Furthermore, gross vehicle weight can be estimated within 5% error corresponding to A class of the European classification if effects of temperature and noise are removed and vehicle properties such as speed, axle arrangement and running location are considered properly.

• Proceedings of the KIPE Conference
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• 2013.07a
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• pp.114-115
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• 2013

This paper proposes an estimation method of back emf for the sensorless controlled high speed PMSM drive in turbo compressors with air bearings. The back emf of PMSM motor varies due to the temperature variation, which deteriorates the control performance of sensorless controlled PMSM drives. The proposed method is based on the current model of the PMSM motor. The simulation results show that the proposed method estimates the back emf of sensorless controlled PMSM drives with reasonable accuracy for parameter adaptation.

• Journal of Power Electronics
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• v.2 no.2
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• pp.95-103
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• 2002

The paper deals with the control of the synchronous reluctance machine without position senser. A method for the computation of the transformation angle out of terminal voltages and currents is presented. The injection of test signals allows operation at zero speed. Fundamental for this control scheme is the angle estimation method over the whole operating range including field-weakening is discussed in detail. The implementation of the angle estimation method in a rotor-oriented control scheme and practical results are demonstrated.

• Proceedings of the KIEE Conference
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• 2006.10d
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• pp.163-165
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• 2006

This paper presents a implementation of speed sensorless vector control algorithm of induction motor using MATLAB/SIMULINK amd dSPACE DSl104 R&D board. The estimation of rotor flux linkage and rotor speed is carried out using model reference adaptive system(MRAS) method. Estimated rotor speed is used to speed controller of induction motor. Simulation results are presented to confirm speed sensorless vector control algorithm.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.286-291
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• 1998

SAR has provided weather independent images on land and sea surface, which can be used for extracting various useful informations. Recently attempts to estimate wind field parameters from SAR images over the oceans have been made by various groups over the world. Although scatterometer loaded in ERS-1 and ERS-2 observes the global wind vector field at spatial resolution of 50 Km with accuracies of $\pm$2m/s in speed, the spatial resolution may not be good enough for applications in coastal regions. It is weil known the sea surface roughness is closely correlated to the wind field, but the wind retrieval algorithms from SAR images are yet in developing stage. Since the radar backscattering properties of the SAR images are principally the same as that of scatterometer, some previous studies conducted by other groups report the success in mesoscale coastal wind field retrievals using ERS SAR images. We have tested SWA (SAR Wind Algorithm) and CMOD4 model for estimation of wind speed using an ERS-1 SAR image acquired near Cheju Island, Korea, in October 11, 1994. The precise estimation of sigma nought and the direction of wind are required for applying the CMOD4 model to estimate wind speed. The wind speed in the test sub-image is estimated to be about 10.5m/s, which relatively well agrees to the observed wind speed about 9.0m/s at Seoguipo station. The wind speed estimation through the SWA is slightly higher than that of CMOD4 model. The sea surface condition may be favorable to SWA on the specific date. Since the CMOD4 model requires either wind direction or wind speed to retrieve the wind field, we should estimate the wind speed first using other algorithm including SWA. So far, it is not conclusive if the SWA can be used to provide input wind speed data for CMOD4 model or not. Since it is only initial stage of implementing the wind field retrieval algorithms and no in-situ observed data is currently avaliable, we are not able to evaluate the accuracy of the results at the moment. Therefore verification studies should be followed in the future to extract reliable wind field information in the coastal region using ERS SAR images.