• Transactions of the Korean Society of Automotive Engineers
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• v.4 no.2
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• pp.115-126
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• 1996

In order to obtain the principal design data for developing the Autonomous Robot Vehicle(ARV), Sensitivity analysis on the trajectory error and friction force with respect to the dynamic parameters is performed. In the straight motion, the trajectory error has been proved to be much affected by the mass variance of the ARV while the lateral friction force is much affected by the location of the mass center. In the curved motion, the effect of mass and moment of inertia is considered importantly. In addition, the lateral offset gives more effect than the geometric dimension of the ARV on the trajectory errors and friction force.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.5
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• pp.1-11
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• 2015

This paper is focused on how the trajectory of launch vehicle could be optimally estimated by the quadratic regression of trajectory data mining for the operation of telemetry ground system in NARO space center during real-time. To receive the telemetry data, the telemetry ground system has to track the space launch vehicle without tracking loss, and it is possible by the well-designed algorithm to estimate a flight position in real-time. For this reason, the quadratic regression model instead of interpolation was considered to estimate the exact position data of launch vehicle and the improvement of antenna performance. For analysis, the real trajectory data which had been logged during NARO 1st launch mission were used, the estimation result of launcher current position was analyzed by the mathematical modeling. In conclusion, the algorithm using quadratic regression based on trajectory data mining showed the better performance than previous interpolation algorithm to estimate the next flight position and the antenna driving performance.

• Journal of Korean Society of Transportation
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• v.29 no.3
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• pp.73-82
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• 2011

Recent advancement in traffic surveillance systems has allowed the researchers to obtain more detailed vehicular movement such as individual vehicle trajectory data. Understanding the characteristics of interactions between leading and following vehicles in the traffic flow stream is a backbone for designing and evaluating more sophisticated traffic and vehicle control strategies. This study proposes a methodology for estimating rear-end crash potential, as a probabilistic measure, in real-time based on the analysis of vehicular movements. The methodology presented in this study consists of three components. The first predicts vehicle position and speed every second using a Kalman filtering technique. The second estimates the probability for the vehicle's trajectory to belong to either 'changing lane' or 'going straight'. A binary logistic regression (BLR) is used to model the lane-changing decision of the subject vehicle. The other component calculates crash probability by employing an exponential decay function that uses time-to-collision (TTC) between the subject vehicle and the front vehicle. The result of this study is expected to be adapted in developing traffic control and information systems, in particular, for crash prevention.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1879-1885
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• 2017

This paper discusses the trajectory tracking system of unmanned ground vehicles based on predictive control. Because the unmanned ground vehicles can not satisfactorily complete the path tracking task, highly efficient and stable trajectory control system is necessary for unmanned ground vehicle to be realized intelligent and practical. According to the characteristics of unmanned vehicle, this paper built the kinematics tracking models firstly. Then studied algorithm solution with the tools of the optimal stability analysis method and proposed a tracking control method based on the model predictive control. The controller used a kinematics-based prediction model to calculate the predictive error. This controller helps the unmanned vehicle drive along the target trajectory quickly and accurately. The designed control strategy has the true robustness, simplicity as well as generality for kinematics model of the unmanned vehicle. Furthermore, the computer Simulink/Carsim results verified the validity of the proposed control method.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.3
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• pp.247-255
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• 2013

The flight control of re-entry vehicles poses a challenge to conventional gain-scheduled flight controllers due to the widely spread aerodynamic coefficients. In addition, a wide range of uncertainties in disturbances must be accommodated by the control system. This paper presents the design of a roll channel controller for a non-axisymmetric reentry vehicle model using the trajectory linearization control (TLC) method. The dynamic equations of a moving mass system and roll control model are established using the Lagrange method. Nonlinear tracking and decoupling control by trajectory linearization can be viewed as the ideal gain-scheduling controller designed at every point along the flight trajectory. It provides robust stability and performance at all stages of the flight without adjusting controller gains. It is this "plug-and-play" feature that is highly preferred for developing, testing and routine operating of the re-entry vehicles. Although the controller is designed only for nominal aerodynamic coefficients, excellent performance is verified by simulation for wind disturbances and variations from -30% to +30% of the aerodynamic coefficients.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.883-888
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• 2004

In this paper, we propose a method to avoid obstacles that have unstable limit cycles in a chaos trajectory surface. We assume all obstacles in the chaos trajectory surface have a Van der Pol equation with an unstable limit cycle. When a chaos UAVs meet an obstacle in an Arnold equation, Chua's equation and hyper-chaos equation trajectory the obstacle reflects the UAV( Unmanned Aerial Vehicle).

• Journal of Mechanical Science and Technology
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• v.20 no.10
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• pp.1557-1566
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• 2006

We present a procedure for the application of global orthogonal polynomial into an atmospheric re-entry maneuvering problem. This trajectory optimization is imbedded in a family of canonically parameterized optimal control problem. The optimal control problem is transcribed to nonlinear programming via global orthogonal polynomial and is solved a sparse nonlinear optimization algorithm. We analyze the optimal trajectories with respect to the performance of re-entry maneuver.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.2
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• pp.150-159
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• 2010

The approach and landing phase of a re-entry vehicle is composed of Steep Glideslope phase, Circular Flare phase, Flare Maneuver phase. The trajectory planning algorithm with geometric parameters is studied in this paper for on-board trajectory planning. This algorithm generate reference trajectory rapidly considering safe landing of re-entry vehicle. In this paper, the Mamdani Fuzzy PD type controller for longitudinal and lateral control is designed which has robustness of nonlinear system. In addition, the simulation is performed including initial downrange and crossrange errors, and the results shows that the proposed fuzzy logic controller has good performance.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.4
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• pp.8-18
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• 2005

Pedestrians involved in traffic accidents manifest unique trajectory characteristics depending on the collision speed, vehicle configuration, and pedestrian postures. However, the existing analytical models for pedestrian movements do not fully include the rotational characteristics of the pedestrians because they assume a two dimensional parabolic trajectory. This faulty assumption in the development of these models limits their applicability and reliability This study investigated the pedestrians movement at collision by computer simulation. The simulations are carried out by using HADYMO, which is a special simulation software system for dynamic movement analysis. Vehicles and pedestrians are modeled and verified via real crash worthiness experiments. Simulations are performed for various collision speeds, vehicle configuration, and pedestrian postures. Since the simulation uses multi-body dynamics, It can express irregular phenomena of the bodies quite well. The results can be exploited for vehicle design and traffic accident reconstruction.

• Journal of Auto-vehicle Safety Association
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• v.11 no.3
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• pp.30-36
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• 2019

This paper suggests a car-following algorithm for urban environment, with multiple target candidates. Until now, advanced driver assistant systems (ADASs) and self-driving technologies have been researched to cope with diverse possible scenarios. Among them, car-following driving has been formed the groundwork of autonomous vehicle for its integrity and flexibility to other modes such as smart cruise system (SCC) and platooning. Although the field has a rich history, most researches has been focused on the shape of target trajectory, such as the order of interpolated polynomial, in simple single-lane situation. However, to introduce the car-following mode in urban environment, realistic situation should be reflected: multi-lane road, target's unstable driving tendency, obstacles. Therefore, the suggested car-following system includes both in-lane preceding vehicle and other factors such as side-lane targets. The algorithm is comprised of three parts: path candidate generation and optimal trajectory selection. In the first part, initial guesses of desired paths are calculated as polynomial function connecting host vehicle's state and vicinal vehicle's predicted future states. In the second part, final target trajectory is selected using quadratic cost function reflecting safeness, control input efficiency, and initial objective such as velocity. Finally, adjusted path and control input are calculated using model predictive control (MPC). The suggested algorithm's performance is verified using off-line simulation using Matlab; the results shows reasonable car-following motion planning.