• Journal of Power Electronics
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• v.11 no.5
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• pp.719-725
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• 2011

This paper describes a novel Direct Torque Control (DTC) method for adjustable speed Doubly-Fed Induction Machine (DFIM) drives which is supplied by a two-level Space Vector Modulation (SVM) voltage source inverter (DTC-SVM) in the rotor circuit. The inverter reference voltage vector is obtained by using input-output feedback linearization control and a DFIM model in the stator a-b axes reference frame with stator currents and rotor fluxes as state variables. Moreover, to make this nonlinear controller stable and robust to most varying electrical parameter uncertainties, a two layer recurrent Artificial Neural Network (ANN) is used to estimate a certain function which shows the machine lumped uncertainty. The overall system stability is proved by the Lyapunov theorem. It is shown that the torque and flux tracking errors as well as the updated weights of the ANN are uniformly ultimately bounded. Finally, effectiveness of the proposed control approach is shown by computer simulation results.

• International Journal of Automotive Technology
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• v.8 no.5
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• pp.555-562
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• 2007

The Variable Valve Timing(VVT) system for high performance is a key technology used in newly developed engines. The system realizes higher torque, better fuel economy, and lower emissions by allowing an additional degree of freedom in valve timing during engine operation. In this study, a model-based control method is proposed to enable a fast and precise VVT control system that is robust with respect to manufacturing tolerances and aging. The VVT system is modeled by a third-order nonlinear state equation intended to account for nonlinearities of the system. Based on the model, a controller is designed for position control of the VVT system. The sliding mode theory is applied to controller design to overcome model uncertainties and unknown disturbances. The experimental results suggest that the proposed sliding mode controller is capable of improving tracking performance. In addition, the sliding mode controller is robust to battery voltage disturbance.

• The Transactions of the Korean Institute of Power Electronics
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• v.12 no.4
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• pp.324-331
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• 2007

This paper studies new estimation method for state of charge (SOC) of the hybrid electric vehicle lithium battery using sliding mode observer. A simple R-C Lithium battery modeling technique is established and the errors caused by simple modeling was compensated by the sliding mode observer. The structure of the sliding mode observer is simple, but it shows robust control property against modeling errors and uncertainties. The performance of the system has been verified by the UUDS test. The test results of the proposed observer system shows robust tracking performance under real driving environments.

• Journal of Power Electronics
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• v.18 no.1
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• pp.251-265
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• 2018

To enable a master-slave control independent microgrid system (MSCIMGS) to supply electricity continuously, the microgrid inverter should perform mode transfer between grid-connected and islanding operations. Transient oscillations should be reduced during transfer to effectively conduct a seamless mode transfer. This study uses a typical MSCIMGS as an example and improves the mode transfer strategy in three aspects: (1) adopts a status-tracking algorithm to improve the switching strategy of the outer loop, (2) uses the voltage magnitude and phase pre-synchronization algorithm to reduce transient shock at the time of grid connection, and (3) applies the hybrid-sensitivity $H_{\infty}$ robust controller instead of the current inner loop to improve the robustness of the controller. Simulations and experiments show that the proposed strategy is more practical than the traditional proportional-derivative control mode transfer and effective in reducing voltage and current oscillations during the transfer period.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.2
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• pp.1-7
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• 2008

This paper presents a method to design a repetitive controller that is robust to variations in the system parameters. The uncertain parameters are specified probabilistically by their probability distribution functions. Instead of working with the distribution functions directly, the repetitive controller is designed from a set of models that are generated from the specified probability functions. With this multiple-model design approach, any number of uncertain parameters that follow any type of distribution functions can be treated. furthermore, the controller is derived by minimizing a frequency-domain based cost function that produces monotonic convergence of the tracking error as a function of repetition number. Numerical illustrations show how the proposed multiple-model design method produces a repetitive controller that is significantly more robust than an optimal repetitive controller designed from a single nominal model of the system.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.422-428
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• 2008

In this paper, we propose the robust object detection algorithm with spatial gradient information. To do this, first, we eliminate error values that appear due to complex environment and various illumination change by using prior methods based on hue and intensity from the input video and background. Visible shadows are eliminated from the foreground by using an RGB color model and a qualified RGB color model. And unnecessary values are eliminated by using the HSI color model. The background is removed completely from the foreground leaving a silhouette to be restored using spatial gradient and HSI color model. Finally, we validate the applicability of the proposed method using various indoor and outdoor conditions in a complex environments.

• Journal of Sensor Science and Technology
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• v.28 no.3
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• pp.164-170
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• 2019

In this paper, a lane detection and tracking algorithm based on vision sensors and employing a robust filter for inner edge detection is proposed for developing a lane departure warning system (LDWS). The lateral offset value was precisely calculated by applying the proposed filter for inner edge detection in the region of interest. The proposed algorithm was subsequently compared with an existing algorithm having lateral offset-based warning alarm occurrence time, and an average error of approximately 15ms was observed. Tests were also conducted to verify whether a warning alarm is generated when a driver departs from a lane, and an average accuracy of approximately 94% was observed. Additionally, the proposed LDWS was implemented as an embedded system, mounted on a test vehicle, and was made to travel for approximately 100km for obtaining experimental results. Obtained results indicate that the average lane detection rates at day time and night time are approximately 97% and 96%, respectively. Furthermore, the processing time of the embedded system is found to be approximately 12fps.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1231-1234
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• 2003

A sensor working in outdoor may generate some faulty signal owing to dust and high temperature. This paper describes an intelligent sensor system and controller which has a reconstruction mechanism for faulty signal. The faulty signals are dievided into two types as linear distortion and non linear distortion, respectively. The linear distorted signal is due to dust, and non linear distorted signal is due to physical breakdown of sensor or high temperature. These distorted signal have been reconstructed by the proposed method based on polynomial regression method and principal component analysis approach.. The proposed method has been applied to sun tracking system working in outdoor. For a robust and precision control of sun tracker, a fuzzy controller was also proposed. The fuzzy controller controls the tracker by using the collected sensor signal. The tolerance of the position control is within 1.5 degree. To show the validity of the developed system, some experiments in the field were illustrated.

• Journal of Korea Multimedia Society
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• v.21 no.10
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• pp.1162-1170
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• 2018

This paper presents an augmented reality system for multiple users. The proposed system performs ar image-based pose estimation of users and pose of each user is shared with other uses via a network server. For camera-based pose estimation, we install multiple markers in a pre-determined space and select the marker with the best appearance. The marker is detected by corner point detection and for robust pose estimation. the marker's corner points are tracked by optical flow tracking algorithm. Experimental results show that the proposed system successfully provides an augmented reality application to multiple users even when users are rapidly moving and some of markers are occluded by users.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.1
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• pp.47-52
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• 2010

In this paper, we introduce a new approach for real-time 3D face pose discrimination based on active IR illumination from a monocular view of the camera. Under the IR illumination, the pupils appear bright. We develop algorithms for efficient and robust detection and tracking pupils in real time. Based on the geometric distortions of pupils under different face orientations, an eigen eye feature space is built based on training data that captures the relationship between 3D face orientation and the geometric features of the pupils. The 3D face pose for an input query image is subsequently classified using the eigen eye feature space. From the experiment, we obtained the range of results of discrimination from the subjects which close to the camera are from 94,67%, minimum from 100%, maximum.