• Proceedings of the KIEE Conference
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• 1992.07b
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• pp.1196-1198
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• 1992

In field oriented control of Induction motors, speed sensor is required, which reduces the sturdiness of drive system and together with the expenditure of hardware for faultless transmission and processing of sensor signals it causes considerable expenses. These expensive sensors can be replaced by speed sensorless concept. And for good control, the knowledge of the rotor flux component of the rotor resistance are needs. Thus, this paper is based on a Extended Kalman Filter( EKF ) that estimates the state variables that are required for the control by only measuring the line voltages and currents of the machine. The rotor time constant and speed estimated by the EKF shows satisfactory agreement with the real values, with the simulation approaches.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.83-85
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• 2005

Exploration involving mapping and localization in an unknown environment is an important task in mobile robots. For this, robot must be able to build a reliable map of surroundings and to estimate the position of it. In this paper, we developed technique for gird-based localization of a mobile robot with ultrasonic sensors using EKF(Extended Kalman Filter). We also describe the information about landmarks detected in the environment. Finally, the robot experiments show the efficiency of our approach in the real environment.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.97.3-97
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• 2001

An experimental study was performed to establish the validity of an on-line state estimator for a semibatch MMA-MA copolymerization reactor by using on-line densitometer and viscometer under two different operating conditions; one without additional solvent feed and the other with solvent fed additionally. A conventional extended Kalman filter (EKF) was used as the state estimator and the experiment was conducted for the purpose of application to the control of copolymer properties. Further analysis was made by using off-line measurement data for the mole fraction of MMA in the remaining monomers and the solid content. It was found that the EKF could provide a good estimate for the states of the copolymerzation system ...

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.75-86
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• 1999

An air-data estimator for generic air-breathing hypersonic vehicles (AHSVs) is developed and demonstrated with an example vehicle configuration. The AHSV air-data estimation strategy emphasized improvement of the angle of attack estimate accuracy to a degree necessitated by the stringent operational requirements of the air-breathing propulsion. the resulting estimation problem involves highly nonlinear diffusion process (propagation); consequently, significant distortion of a posteriori conditional density is suspected. A simulation based statistical analysis tool is developed to characterize the nonlinear diffusion process. The statistical analysis results indicate that the diffusion process preserves the symmetry and unimodality of initial probability density shape state variables, and provide the basis for applicability of an Extended Kalman Filter (EKF). An EKF is designed for the AHSV air-data system and the air data estimation capabilities are demonstrated.

• Journal of the Korean Society of Safety
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• v.13 no.1
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• pp.54-63
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• 1998

This paper proposes a theoretical analysis of a closed loop adaptive speed control system for control the inverter driven permanent magnet synchronous motor(PMSM). This control system utilizes a mechanically sensorless state observer for the generation of all controller feedback information. The observer processes measurements of stator frame voltage and current to produce estimates of rotor position and speed and rotor frame currents. It is shown that the identity observer, when properly formulated, has the same linearized error dynamics as the extended kalman filter(EKF). Consequently, it is shown that the gains within the identity observer can be designed in a manner identical to that of the EKF. In this way, the designability of the nonlinear observer is assured, as is the optimality of its performance for small errors. A sequence of simulation are performed and they demonstrate the successful performance.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.3B no.2
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• pp.97-102
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• 2003

In this paper, a recurrent artificial neural network (RNN) based self-tuning speed controller is proposed for the high-performance drives of induction motors. The RNN provides a nonlinear modeling of a motor drive system and could provide the controller with information regarding the load variation system noise, and parameter variation of the induction motor through the on-line estimated weights of the corresponding RNN. Thus, the proposed self-tuning controller can change the gains of the controller according to system conditions. The gain is composed with the weights of the RNN. For the on-line estimation of the RNN weights, an extended Kalman filter (EKF) algorithm is used. A self-tuning controller is designed that is adequate for the speed control of the induction motor The availability of the proposed controller is verified through MATLAB simulations and is compared with the conventional PI controller.

• Proceedings of the KIPE Conference
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• 2013.07a
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• pp.317-318
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• 2013

State-of-Charge (SOC) is one of the most important indicators for the battery management system. Thus its precise estimation is crucial not only for effectively utilizing the energy but also preventing critical situations from happening to the powertrain of the vehicle. However, lead-acid battery is time-variant and highly nonlinear, and the hysteresis phenomenon causes large errors in estimating SOC. This paper proposes a novel SOC estimation technique for the lead-acid battery by using Extended Kalman Filter (EKF) considering hysteresis effect. The validity of the proposed technique is verified through the experiments.

• International Journal of Aerospace System Engineering
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• v.7 no.2
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• pp.6-12
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• 2020

The precise prediction of future position of satellite depends on the accurate determination of orbit, which is also helpful in performing orbit maneuvers and trajectory correction maneuvers. For estimating the orbit of satellite many methods are being used. Some of the conventional methods are based on (i) Differential Correction (DC) (ii) Extended Kalman Filter (EKF). In this paper, Differential Evolution (DE) is used to determine the orbit. Orbit Determination using DC and EKF requires some initial guess of the state vector to initiate the algorithm, whereas DE does not require an initial guess since a wide range of bounds for the design unknown variables (orbital elements) is sufficient. This technique is uniformly valid for all orbits viz. circular, elliptic or hyperbolic. Simulated observations have been used to demonstrate the performance of the method. The observations are generated by including random noise. The simulation model that generates the observations includes the perturbation due to non-spherical earth up to second zonal harmonic term.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.2
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• pp.225-234
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• 2009

Real-time Aerial Monitoring System (RAMS) is to perform the rapid mapping in an emergency situation so that the geoinformation such as orthophoto and/or Digital Elevation Model is constructed in near real time. In this system, the GPS/INS plays an very important role in providing the position as well as the attitude information. Therefore, in this study, the performance of an IMU sensor which is supposed to be installed on board the RAMS is evaluated. And the integration algorithm of GPS/INS are tested with simulated dataset to find out which is more appropriate in real time mapping. According to the static and kinematic results, the sensor shows the position error of 3$\sim$4m and 2$\sim$3m, respectively. Also, it was verified that the sensor performs better on the attitude when the magnetic field sensor are used in the Aerospace mode. In the comparison of EKF and UKF, the overall performances shows not much differences in straight as well as in curved trajectory. However, the calculation time in EKF was appeared about 25 times faster than that of UKF, thus EKF seems to be the better selection in RAMS.

• The Journal of Korea Robotics Society
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• v.9 no.1
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• pp.28-38
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• 2014

This paper verifies the performance of Extended Kalman Filter(EKF) and MCL(Monte Carlo Localization) approach to localization of an underwater vehicle through experiments. Especially, the experiments use acoustic range sensor whose measurement accuracy and uncertainty is not yet proved. Along with localization, the experiment also discloses the uncertainty features of the range measurement such as bias and variance. The proposed localization method rejects outlier range data and the experiment shows that outlier rejection improves localization performance. It is as expected that the proposed method doesn't yield as precise location as those methods which use high priced DVL(Doppler Velocity Log), IMU(Inertial Measurement Unit), and high accuracy range sensors. However, it is noticeable that the proposed method can achieve the accuracy which is affordable for correction of accumulated dead reckoning error, even though it uses only range data of low reliability and accuracy.