• Title/Summary/Keyword: 무인 항공기 성능 지표

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Design and Performance Verification of L1 Adaptive Flight Control Law Considering the Change of Center of Gravity for Unmanned Tailless Aircraft (무인 무미익 항공기의 무게중심 변화를 고려한 L1 적응제어 비행제어 법칙 설계 및 성능 검증)

  • Ko, Dong-hyeon;Kang, Ji-soo;Choi, Keeyoung
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.47 no.2
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    • pp.114-121
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    • 2019
  • Tailless aircraft have advantages of low visibility compared to conventional aircraft, but drawback of poor stability as well which makes designing controller difficult. The controller design is more difficult, especially when the center of gravity moves due to store release or fuel consumption during flight. In this paper, an L1 adaptive controller is proposed as a way to overcome these problems. The reliability and performance of the controllers were verified by non-linear simulations. RPV Flying Quality Design criteria were used for design criteria. Using the simulation, it is shown that the adaptive controller maintains stability of the unmanned aircraft for sudden large change in the inertial properties. It is also shown that the calculation burden can be reduced when it is used with the gain scheduling method.

Model-Reference Adaptive Pitch Attitude Control of Fixed-Wing UAV (고정익 무인 항공기 피치 자세의 모델-참조 적응 제어)

  • Kim, Byung-Wook;Park, Sang-Hyuk
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.47 no.7
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    • pp.499-507
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    • 2019
  • Despite the well-known mathematical model of fixed-wing aircraft, there are various studies to meet desired performances by considering the modeling errors in the extended flight envelope. This paper proposes a new adaptation mechanism of model-reference adaptive control, which applies the Levenberg-Marquardt algorithm to the pitch attitude control of fixed-wing UAV. In addition, reference model in the adaptation law is set by referring to the dynamic properties of the plant model. The performance of the proposed adaptive control law is verified through simulations and flight tests.

Reconfigurable Simulator for Safety Evaluation of eVTOL Aircraft (eVTOL 항공기 안전성 평가를 위한 가변형 시뮬레이터 구축)

  • Hyeji Kim;Jeongmin Kim;Dayeon Yoon;Jongjun Ha;Dongjin Lee;Jangho Lee
    • Journal of Advanced Navigation Technology
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    • v.28 no.1
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    • pp.95-101
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    • 2024
  • This paper aims to establish a reconfigurable flight simulation environment to conduct safety evaluation of various electric vertical take-off and landing (eVTOL) aircraft. Since the inceptor, aircraft dynamics model, and controller applied to each eVTOL aircraft are different, it was configured to be variable so that a simulation can be executed for each eVTOL aircraft. Test elements and performance indicators were set to perform safety evaluation of eVTOL aircraft. Ground auxiliary equipments were designed and implemented in a simulation environment according to test procedures for each test element. In addition, to analyze safety performance, a simulation flight data collection environment based on MATLAB/Simulink and a tool for safety performance analysis were implemented. Test flight and analysis were conducted in the implemented simulation environment in this paper. Finally, this study shows the environment was verified by confirming that it was performed normally.

A Study on Technology Forecasting of Unmanned Aerial Vehicles (UAVs) Using TFDEA (TFDEA를 이용한 무인항공기 기술예측에 관한 연구)

  • Jung, Byungki;Kim, H.C.;Lee, Choonjoo
    • Journal of Korea Technology Innovation Society
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    • v.19 no.4
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    • pp.799-821
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    • 2016
  • Unmanned Aerial Vehicles (UAVs) are essential systems for Intelligence, Surveillance, and Reconnaissance (ISR) operations in current battlespace. And its importance will be getting extended because of complexity and uncertainty of battlespace. In this study, we forecast the advancement of 96 UAVs during the period of 32 years from 1982 to 2014 using TFDEA. TFDEA is a quantitative technology forecasting method which is characterized as non-parametric and non-statistical mathematical programming. Inman et al. (2006) showed that TFDEA is more accurate in forecasting compared with classical econometrics (e.g. regression). This study got 4.06% point of annual technological rate of change (RoC) for UAVs by applying TFDEA. And most UAVs in the period are inefficient according to the global SOA frontiers. That is because the countries which develop UAVs are in the middle class of technological level, so more than 60% of world UAVs markets are shared by North America and Europe which are advanced countries in terms of technological maturity level. This study could give some insights for UAVs development and its advancement. And also can be used for evaluating the adequacy of Required Operational Capability (ROC) of suggested future systems and managing the progress of Research and Development (R&D).

CNN based dual-channel sound enhancement in the MAV environment (MAV 환경에서의 CNN 기반 듀얼 채널 음향 향상 기법)

  • Kim, Young-Jin;Kim, Eun-Gyung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.23 no.12
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    • pp.1506-1513
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    • 2019
  • Recently, as the industrial scope of multi-rotor unmanned aerial vehicles(UAV) is greatly expanded, the demands for data collection, processing, and analysis using UAV are also increasing. However, the acoustic data collected by using the UAV is greatly corrupted by the UAV's motor noise and wind noise, which makes it difficult to process and analyze the acoustic data. Therefore, we have studied a method to enhance the target sound from the acoustic signal received through microphones connected to UAV. In this paper, we have extended the densely connected dilated convolutional network, one of the existing single channel acoustic enhancement technique, to consider the inter-channel characteristics of the acoustic signal. As a result, the extended model performed better than the existed model in all evaluation measures such as SDR, PESQ, and STOI.

Detection and Grading of Compost Heap Using UAV and Deep Learning (UAV와 딥러닝을 활용한 야적퇴비 탐지 및 관리등급 산정)

  • Miso Park;Heung-Min Kim;Youngmin Kim;Suho Bak;Tak-Young Kim;Seon Woong Jang
    • Korean Journal of Remote Sensing
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    • v.40 no.1
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    • pp.33-43
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    • 2024
  • This research assessed the applicability of the You Only Look Once (YOLO)v8 and DeepLabv3+ models for the effective detection of compost heaps, identified as a significant source of non-point source pollution. Utilizing high-resolution imagery acquired through Unmanned Aerial Vehicles(UAVs), the study conducted a comprehensive comparison and analysis of the quantitative and qualitative performances. In the quantitative evaluation, the YOLOv8 model demonstrated superior performance across various metrics, particularly in its ability to accurately distinguish the presence or absence of covers on compost heaps. These outcomes imply that the YOLOv8 model is highly effective in the precise detection and classification of compost heaps, thereby providing a novel approach for assessing the management grades of compost heaps and contributing to non-point source pollution management. This study suggests that utilizing UAVs and deep learning technologies for detecting and managing compost heaps can address the constraints linked to traditional field survey methods, thereby facilitating the establishment of accurate and effective non-point source pollution management strategies, and contributing to the safeguarding of aquatic environments.