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목성훈(Sung-Hoon Mok),김태호(Taeho Kim),방효충(Hyochoong Bang),송태성(Taeseong Song),이종국(Jongkuck Lee),송덕기(Deokki Song),서중보(Joongbo Seo) 한국항공우주학회 2019 韓國航空宇宙學會誌 Vol.47 No.4
자세제어 M&S(Modeling & Simulation)는 모델링 정밀도에 따라 간단한 자세제어 알고리즘 설계부터 실제 탑재 소프트웨어 검증까지 활용될 수 있다. 본 논문은 CMG 기반 위성 자세제어를 위한 M&S 소프트웨어를 소개한다. 개발된 소프트웨어는 크게 6자유도 모델링과 자세제어 알고리즘으로 나뉠 수 있다. 6자유도 모델링은 CMG 구동기 모델링과 우주환경 모델링(궤도외란/자세외란)을 포함한다. 자세제어 알고리즘은 CMG 모터속도 제어기(내부루프)부터 토크명령생성 및 스티어링 법칙(외부루프)을 포함한다. M&S는 하위 레벨인 CMG 모터제어부터 상위 레벨인 지구관측임무 수행까지 단계적으로 수행되며, 적용 레벨에 따라 3개 모듈로 나뉜다. 본 M&S 소프트웨어는 초기 자세제어 알고리즘 개발을 목표로 개발되었는데, 향후 상세한 위성체/구동기 모델링 및 우주환경 모델링을 반영해 점차 활용 범위를 넓힐 수 있을 것으로 기대된다. Attitude control modeling and simulation (M&S) can be extensively applied in overall development process, from simple algorithm design to on-board software verification. This paper introduces CMG-based attitude control M&S software, which consists of 6-DOF modeling (CMG and space environments modeling), and attitude control algorithm. The M&S software is divided into three modules, from an inner CMG motor control module to an outer earth observation mission module. While an application of this developed software is currently limited to the initial-phase attitude controller development, its application area can be extended to the later-phases by considering sophisticated model information in future.
목성훈(Sung-Hoon Mok),방효충(Hyochoong Bang),권재현(Jayhyun Kwon),유명종(Myeongjong Yu) 제어로봇시스템학회 2013 제어·로봇·시스템학회 논문지 Vol.19 No.8
Underwater TRN (Underwater Terrain Referenced Navigation) estimates an underwater vehicle state by measuring a distance between the vehicle and undersea terrain, and comparing it with the known terrain database. TRN belongs to absolute navigation methods, which are used to compensate a drift error of dead reckoning measurements such as IMU (Inertial Measurement Unit) or DVL (Doppler Velocity Log). However, underwater TRN is different to other absolute methods such as USBL (Ultra-Short Baseline) and LBL (Long Baseline), because TRN is independent of the external environment. As a magnetic-field-based navigation, TRN is a kind of geophysical navigation. This paper develops an EKF (Extended Kalman Filter) formulation for underwater TRN. A filter propagation part is composed by an inertial navigation system, and a filter update is executed with echo-sounder measurement. For large-initial-error cases, an adaptive EKF approach is also presented, to keep the filter be stable. At the end, simulation studies are given to verify the performance of the proposed TRN filter. With simplified sensor and terrain database models, the simulation results show that the underwater TRN could support conventional underwater navigation methods.
목성훈(Sung-Hoon Mok),방효충(Hyochoong Bang),유명종(Myeongjong Yu) 제어로봇시스템학회 2017 제어·로봇·시스템학회 논문지 Vol.23 No.4
This paper studies a vision-based terrain referenced navigation. Generally, in terrain referenced navigation, a radar altimeter is utilized as a primary sensor. However, the single sensor information is not enough to compensate for the degradation of navigation performance caused by the increased sensor noise when the aircraft is operated at high altitude. To cope with that performance degradation, this paper adopts a vision sensor as a supplement sensor to expand the amount of measurement information. The measurement model is formed by the features’ estimated positions based on stereopsis between sequential images. Monte Carlo simulation is conducted using 200 samples considering evenly distributed aircraft initial positions and initial position errors. This is to test the navigation performance of a highly nonlinear system characterized by the terrain altitude variation. The simulation result verifies that navigation error is reduced with the aid of the vision sensor when compared to the conventional filter in which the radar altimeter is used solely.