• Journal of Astronomy and Space Sciences
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• v.37 no.3
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• pp.187-197
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• 2020

In order to avoid the high cost and high risk of demonstration mission of rendezvous-docking technology, missions using nanosatellites have recently been increasing. However, there are few successful mission cases due to many limitations of nanosatellites like small size, power limitation, and limited performances of sensor, thruster, and controller. To improve the probability of rendezvous-docking mission success using nanosatellite, a rendezvous-docking phase analysis tool for nanosatellites is developed. The tool serves to analyze the relative position and attitude control of the chaser satellite at the docking phase. In this tool, the Model Predictive Controller (MPC) is implemented as a controller, and Extended Kalman Filter (EKF) is adopted as a filter for noise filtering. To verify the performance and effectiveness of the developed tool for nanosatellites, simulation study was conducted. Consequently, we confirmed that this tool can be used for the analysis of relative position and attitude control for nanosatellites in the rendezvous-docking phase.

• Proceedings of the KIPE Conference
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• 2017.07a
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• pp.22-23
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• 2017

본 논문에서는 모듈형 멀티레벨 컨버터 (Modular Multilevel Converter; MMC)의 스위칭 손실을 줄이기 위한 모델 예측 제어 (Model Predictive Control; MPC) 방법을 제안한다. 제안된 방법은 지령 상 전압과 상 전류를 이용하여 매 샘플링 시점 마다 전류가 크게 흐르는 구간 동안 서브모듈의 스위칭 동작을 클램핑 시키는 옵셋전압을 계산한다. 제안된 방법의 스위칭 손실 저감 효과는 기존의 방법과의 손실 데이터 비교를 통하여 검증되었다.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.722-728
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• 2012

Urban sewer systems has a limitation of capacity of rainwater storage and problem of occurrence of untreated sewage, so adopting a storage facility for sewer flooding prevention and urban non-point pollution reduction has a big attention. The Korea Ministry of Environment has recently introduced a new concept of "multi-functional storage facility", which is crucial not only in preventive stormwater management but also in dealing with combined sewer overflow and sanitary sewer discharge, and also has been promoting its adoption. However, reserving a space for a single large-scale storage facility might be difficult especially in urban areas. Thus, decentralized construction of small- and midium-sized storage facilities and its operation have been introduced as an alternative way. In this paper, we propose a model predictive control scheme for an optimized operation of distributed storage facilities and sewer networks. To this aim, we first describe the mathematical model of each component of networks system which enables us to analyze its detailed dynamic behavior. Second, overflow locations and volumes will be predicted based on the developed network model with data on the external inflow occurred at specific locations of the network. MPC scheme based on the introduced particle swarm optimization technique then produces the optimized the gate setting for sewer network flow control, which minimizes sewer flooding and maximizes the potential storage capacity. Finally, the operational efficacy of the proposed control scheme is demonstrated by simulation study with virtual rainstorm event.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.7
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• pp.575-583
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• 2017

This paper describes the design and evaluation of a driving mode decision algorithm for automated driving for merge situations on highways. For the development of a highly automated driving control algorithm for merge situations, the driving mode decision is crucial for merging appropriately. There are two driving modes: lane keeping and lane changing (merging). The merge mode decision is determined based on the state of the surrounding vehicles and the remaining length of the merge lane. In the merge mode decision algorithm, merge possibility and the desired merge position are decided to change the lane safely and quickly. A safety driving envelope is defined based on the desired driving mode using the information on the surrounding vehicles' behaviors. To obtain the desired steering angle and longitudinal acceleration for maintaining the subject vehicle in the safe driving envelope, a motion planning controller is designed using model predictive control (MPC), with constraints that are decided considering the vehicle dynamics, safe driving envelope, and actuator limit. The proposed control algorithm has been evaluated via computer simulation studies.

• 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.

• Korean Chemical Engineering Research
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• v.52 no.5
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• pp.592-602
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• 2014

In the Integrated Gasification Combined Cycle (IGCC), the stability of the gasifier has strong influences on the rest of the plant as it supplies the feed to the rest of the power generation system. In order to ensure a safe and stable operation of the entrained-flow gasifier and for protection of the gasifier wall from the high internal temperature, the solid slag layer thickness should be regulated tightly but its control is hampered by the lack of on-line measurement for it. In this study, a previously published dynamic simulation model of a Shell-type gasifier is reproduced and two different linear model predictive control strategies are simulated and compared for multivariable control of the entrained-flow gasifier. The first approach is to control a measured secondary variable as a surrogate to the unmeasured slag thickness. The control results of this approach depended strongly on the unmeasured disturbance type. In other words, the slag thickness could not be controlled tightly for a certain type of unmeasured disturbance. The second approach is to estimate the unmeasured slag thickness through the Kalman filter and to use the estimate to predict and control the slag thickness directly. Using the second approach, the slag thickness could be controlled well regardless of the type of unmeasured disturbances.

• Journal of Auto-vehicle Safety Association
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• v.7 no.2
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• pp.44-49
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• 2015

This paper presents an evaluation scenario of safety performance for extraordinary service permission of autonomous vehicle driving on a motorway. Based on advanced driver assistance system (ADAS) which is already mass-production, an autonomous vehicle driving on motorway is tested on the public roads and also getting close to mass-production. Before the autonomous vehicle tested, the safety of autonomous driving system should be evaluated based on a proper test scenario. Prior to develop the test scenario, this paper reviews the licensing standards for an autonomous vehicle in California and Nevada, and the international regulations of each ADAS. To develop the scenario, the driving conditions of motorway are categorized into five modes and fundamental evaluation requirements of elements of autonomous driving system are derived. An evaluation scenario, which represents the real driving conditions, has been developed to assess the safety of autonomous vehicle. This scenario has validated by computer simulation using model predictive control (MPC) based autonomous driving algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1603-1623
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• 2020

Multiple Mobile Robots System (MMRS) has shown many attractive features in lots of real-world applications that motivate their rapid and wide diffusion. In MMRS, the Cooperative Localization (CL) is the basis and premise of its high-performance task. However, the statistical characteristics of the system noise should be already known in traditional CL algorithms, which is difficult to satisfy in actual MMRS because of the numerous of disturbances form the complex external environment. So the CL accuracy will be reduced. To solve this problem, an improved Adaptive Active Cooperative Localization (A²CL) algorithm based on information optimization strategy for MMRS is proposed in this manuscript. In this manuscript, an adaptive information fusion algorithm based on the variance component estimation under Extended Kalman filter (VCEKF) method for MMRS is introduced firstly to enhance the robustness and accuracy of information fusion by estimating the covariance matrix of the system noise or observation noise in real time. Besides, to decrease the effect of observation uncertainty on CL accuracy further, an observation optimization strategy based on information theory, the Model Predictive Control (MPC) strategy, is used here to maximize the information amount from observations. And semi-physical simulation experiments were carried out to verity the A²CL algorithm's performance finally. Results proved that the presented A²CL algorithm based on information optimization strategy for MMRS cannot only enhance the CL accuracy effectively but also have good robustness.