• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.735-743
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• 2012

In this paper, we present model predictive control (MPC) applied to lane keeping system (LKS) based on a vision module. Due to a slow sampling rate of the vision system, the conventional LKS using single rate control may result in uncomfortable steering control rate in a high vehicle speed. By applying MPC using multi-rate Kalman filter to active steering control, the proposed MPC-based active steering control system prevents undesirable saturated steering control command. The effectiveness of the MPC is validated by simulations for the LKS equipped with a camera module having a slow sampling rate on the curved lane with the minimum radius of 250[m] at a vehicle speed of 30[m/s].

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.621-626
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• 2019

The driver's steering wheel maneuver is a typical disturbance that causes excessive body motion and traveling instability of a vehicle. Abrupt and extreme operation can cause rollover depending on the geometric and dynamic characteristics, e.g., SUV vehicles. In this study, to cope with the performance limitation of conventional cars, fundamental research on the structurization of a control system was performed as follows. Mathematical modeling of the lateral behavior induced by driver input was carried out. A controller was designed to reduce the body motion based on this model. An algorithm was applied to secure robust control performance against modeling errors due to parameter uncertainty, $H_{\infty}$. Using the decoupled 1/4 car, a dynamic load simulating model considering the body moment was suggested. The simulation result showed the validity of the load-simulating model. The framework for a lateral behavior control system is proposed, including an experimental 1/4 vehicle unit, load simulating module, suspension control module, and hardware-in-the-loop simulation technology.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.281-286
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• 2000

This paper describes a lateral guidance system of an unmanned vehicle, using a neural network model of magneto-resistive sensor and magnetic fields. The model equation was compared with experimental sensing data. We found that the experimental result has a negligible difference from the modeling equation result. We verified that the modeling equation can be used in the unmanned vehicle simulations. As the neural network controller acquires magnetic field values(B$\_$x/, B$\_$y/, B$\_$z/) from the three-axis, the controller outputs a steering angle. The controller uses the back-propagation algorithms of neural network. The learning pattern acquisition was obtained using computer simulation, which is more exact than human driving. The simulation program was developed in order to verify the acquisition of the learning pattern, learning itself, and the adequacy of the design controller. A computer simulation of the vehicle (including vehicle dynamics and steering) was used to verify the steering performance of the vehicle controller using the neural network. Good results were obtained. Also, the real unmanned electrical vehicle using neural network controller verified good results.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2569-2571
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• 2000

In the vehicle steering system, we can consider two methods to steer the vehicle. One is a front wheel steering(FWS), the other is a four wheel steering(4WS). The four wheel steering method has been recently introduced to improve the steering performance. In this paper, we present a design of the four wheel steering controller. First, we constructed the neural network two degree of freedom PID controller to control the 4WS system. Then we compared the performance of conventional PID controller with our proposed controller in terms of yaw rate and side slip velocity. The computer simulation results show that 4WS system controlled by the proposed controller has well driving performances than the other.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.20-20
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• 2017

궤도형 차량의 이동구조는 에너지 소비 측면에서 단점이 있지만 접지압의 감소로 인한 평지 및 야지험지에서도 원활한 주행이 가능한 장점으로 인해 농업분야의 플랫폼에서 많이 사용된다. 곡식을 베는 일과 탈곡하는 일을 한 번에 하는 콤바인도 이러한 무한궤도형 이동구조를 사용한다. 또한 궤도형 차량의 방향전환 및 주행속도 변환은 좌 우 궤도의 회전 속도를 다르게 하여 동시에 제어하기 때문에 정교한 주행 성능을 위해서는 궤도형 차량의 기구학 모델을 고려한 경로 계획이 필요하다. 본 연구에서는 직교형 포장에서 Round harvesting 기법 기반으로 궤도형 차량의 기구학 모델 및 포장정보를 고려한 자율주행 콤바인 경로계획 알고리즘을 개발하고자 하였다. 이를 위해 Labview 기반의 궤도형 차량 시뮬레이션을 구축하여 실제 포장정보를 이용해 생성 된 경로의 적용 가능성을 구명하고자 하였다. 자율주행 콤바인 경로 계획은 콤바인의 길이, 너비, 회전 시 좌 우 궤도의 속도 비, 직진 속도와 회전 속도 비, 회전 각도, 포장의 외부 경계선, 작업 겹침 량, 회경 횟수를 이용하여 좌현 새머리 선회를 포함한 내부 왕복작업 경로를 생성하며 외부 회경 횟수는 2~3회를 가정하였다. 자율주행 시뮬레이션은 차체와 궤도 자체의 미끄러짐과 작동기 지연시간을 단순화 한 궤도형 기구학 모델형태로 구성하였다. 추종 알고리즘은 선견 거리법을 사용하였으며, 측면 변이값과 방향 오차의 선형조합을 이용하여 조향변수를 정의하고 퍼지로직기반으로 좌 우 궤도 속도를 7 단계화하여 조향장치를 모델링하였다. 실험결과 개발 된 경로생성 알고리즘은 실제 취득 된 포장 외부 경계 GPS 위 경도를 이용해 자동으로 생성이 가능하며 간략화 된 콤바인 시뮬레이션에서 직진주행 RMS 위치 오차는 0.05 m, 선회구간에서 직진 구간 진입 시 RMS 위치 오차는 0.11 m, 직진 구간 RMSE 방향 오차는 3.2 deg로 콤바인 예취부 간격인 30 cm보다 작은 위치 오차를 보이며 생성된 경로 전체 추종이 가능함을 나타내었다.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.9
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• pp.723-732
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• 2001

In this paper, the neurofuzzy controller of optically guided AGV is proposed to improve the path-tracking performance A differential steered AGV has front-side and rear-side optical sensors, which can identify the guiding path. Due to the discontinuity of measured data in optical sensors, optically guided AGVs break away easily from the guiding path and path-tracking performance is being degraded. Whenever the On/Off signals in the optical sensors are generated discontinuously, the motion errors can be measured and updated. After sensing, the variation of motion errors can be estimated continuously by the dead reckoning method according to left/right wheel angular velocity. We define the estimated contour error as the sum of the measured contour in the sensing error and the estimated variation of contour error after sensing. The neurofuzzy system consists of incorporating fuzzy controller and neural network. The center and width of fuzzy membership functions are adaptively adjusted by back-propagation learning to minimize th estimated contour error. The proposed control system can be compared with the traditional fuzzy control and decision system in their network structure and learning ability. The proposed control strategy is experience through simulated model to check the performance.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.12
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• pp.1380-1393
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• 2010

This paper presents a design, fabrication and the test results of planar active electronically scanned array(AESA) radar prototype for airborne fighter applications using transmit/receive(T/R) module hybrid technology. LIG Nex1 developed a AESA radar prototype to obtain key technologies for airborne fighter's radar. The AESA radar prototype consists of a radiating array, T/R modules, a RF manifold, distributed power supplies, beam controllers, compact receivers with ADC(Analog-to-Digital Converter), a liquid-cooling unit, and an appropriate structure. The AESA antenna has a 590 mm-diameter, active-element area capable of containing 536 T/R modules. Each module is located to provide a triangle grid with $14.7\;mm{\times}19.5\;mm$ spacing among T/R modules. The array dissipates 1,554 watts, with a DC input of 2,310 watts when operated at the maximum transmit duty factor. The AESA radar prototype was tested on near-field chamber and the results become equal in expected beam pattern, providing the accurate and flexible control of antenna beam steering and beam shaping.

• Journal of Sensor Science and Technology
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• v.12 no.5
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• pp.211-217
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• 2003

This paper is proposed that roadway recognition performance improvement for autonomous vehicle using magnetic markers that are embedded along the road center and the sensors mounted on a vehicle, and which changing of magnetic field that is measured along with vehicle driving. For Retrenchment of equipment cost, interval of markers is more expensive than existing method. In order to this, This paper is proposed that interval of markers is founded using magnetic field analysis, and which arrangement method of six magnetic sensors and control method of neural network. This paper is carried out magnetic field analysis, the acquiring of the training patterns, the training of the neural network and composition of steering control, and is verified that roadway recognition performance can improve using computer simulation with proposed methods.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.9
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• pp.1025-1033
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• 2012

Fabry-Perot cavity(FPC) antennas with artificial magnetic conductor(AMC) surface are designed in order to provide scan capability by $4{\times}1$ array feed inside the cavity. The proposed antenna, excited by $4{\times}1$ thinned array, not only achieve higher directivities but also improve suppression of sidelobe level(SLL) relative to that of the thin array alone. The FPC antenna with the height of a quarter wavelength generate maximum gain of 19 dB, SLL suppression of 14 dB and maximum scan angle of $8^{\circ}$ under the feed phase difference of $90^{\circ}$ at the design frequency of 12 GHz.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2365-2368
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• 2001

This paper describes a fuzzy steering controller for an autonomous mobile robot with MR sensor. Using the magnetic field($B_{x}$, $B_{y}$, $B_{z}$) obtained from the MR sensor, we designed fuzzy controller for driving on the road center. Fuzzy rule base was built to magnetic field($B_{x}$, $B_{y}$, $B_{z}$). To develop an autonomous mobile robot simulation program, we have done modeling MR sensor, dynamic model of mobile robot and coordinate transformation. A computer simulation of the robot (including mobile robot dynamics and steering) was used to verify the steering performance of the mobile robot controller using the fuzzy logic. Good results were obtained by computer simulation. So, we confirmed the robustness of the proposed fuzzy controller by computer simulation. Also, we know that proposed control algorithm was applied to real autonomous mobile robot.