DOCSLIB.ORG

Energy Optimization of a Hybrid Unmanned Aerial Vehicle (UAV)

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Energy Optimization of a Hybrid Unmanned Aerial Vehicle (UAV) Energy Optimization of a Hybrid Unmanned Aerial Vehicle (UAV) Thesis Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University By Danielle Meyer, BS, BA Graduate Program in Electrical and Computer Engineering The Ohio State University 2018 Thesis Committee: Dr. Jiankang Wang, Advisor Dr. Mahesh Illindala ABSTRACT Unmanned Aerial Vehicles (UAV) have continued to receive attention from corpo- rations and governmental agencies due to their wide range of potential applications and hybrid nature. More Electric Aircraft (MEA) promise many benefits (e.g., re- duced weight, decreased fuel consumption, and high reliability) and their development continues to be the trend. Hybrid UAVs are an ideal prototype to implement con- cepts of aircraft electrification due to their small size and the DC nature of their power systems. However, papers addressing the energy optimization UAV electric power sys- tems fail to consider the importance of high accuracy and computational speed. This thesis proposes an energy optimization method to enhance the energy durability of a UAV through a novel approach integrating an optimization formulation and a detailed UAV simulation model, with physical circuitry characteristics. This approach allows for increased computation efficiency while still capturing physical system constraints experienced during real world flight, which are complex and highly nonlinear due to aerial, thermal, and electrical dynamics. Optimization formulations created within this work are based on dynamic programming and moving-horizon model predictive control (MPC). The efficacy of this method is proven on a realistic UAV system. Within the MPC formulation, various charge strategies are implemented and fuel consumption is calculated to provide insight into the trade-offs inherent within the UAV system, wherein battery discharging is required for high demand dash periods, i but additional charge can only be supplied via increased output engine power. That is, minimal fuel consumption must be considered in light of the need for non-optimal output engine power to charge the battery such that a total mission can be completed. Algorithmic considerations regarding horizon size for MPC and algorithmic enhance- ments, considering random loads and renewable generation capacity on-board the UAV are presented. These results regarding enhanced algorithmic elements provide insight into the capability of the algorithm to function within a real-time environment and the benefit of solar arrays to provide additional generation. Using MPC as the optimization technique of choice allows for the development of an algorithm capable of handling both missions with a deterministic load and within online implementations, as deterministic cases represent a downsized problem where algorithmic considera- tions can be studied and iterated to reach satisfactory online implementation. While this thesis approaches the problem from the perspective of UAV design, i.e., opti- mization for a deterministic load profile, the algorithmic enhancements provided here represent initial steps towards online implementation. ii Dedicated to my parents iii ACKNOWLEDGMENTS This thesis would not have been possible without the support of my advisor and mentor, Dr. Jiankang Wang, and my parents. iv VITA 2009 - 2016 . BS, Electrical and Computer Engineer- ing, The Ohio State University 2009 - 2016 . BA, German, The Ohio State Univer- sity 2016 - present . MS, Graduate Research Associate, Department of Electrical and Com- puter Engineering, The Ohio State University PUBLICATIONS Research Publications D. Meyer, J. Choi, J.K. Wang, "Increasing EV public charging with distributed generation in the electric grid," 2015 IEEE Transportation Electrification Conference and Expo (ITEC), Dearborn, MI, 2015, pp. 1-6. D. Meyer, R. Alexander, J.K. Wang, "A simple method for energy optimization to enhance durability of hybrid UAV power systems," 2017 North American Power Symposium (NAPS), Morgantown, WV, 2017, pp. 1-6. D. Mao, D. Meyer, J.K. Wang, "Evaluating PEV's impact on long-term cost of grid assets," 2017 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT), Arlington, VA, pp. 1-5. In Press. D. Meyer, J.K. Wang, "Integrating Ultra-Fast Charging Stations within the Power Grids of Smart Cities: A Review," IET Smart Grid. In Press. Ziran Gao, D. Meyer, J.K. Wang, "Visualizing the Impact of PEV Charging on the Power Grid," 2018 IEEE Transportation Electrification Conference & Expo (ITEC), Long Beach, CA, pp. 1-5. v FIELDS OF STUDY Major Field: Electrical and Computer Engineering vi TABLE OF CONTENTS Page Abstract . .i Dedication . iii Acknowledgments . iv Vita.........................................v List of Tables . ix List of Figures . .x Dynamic Programming - Nomenclature . xii Model Predictive Control - Nomenclature and Symbols . xiii Chapters: 1. Introduction . .1 1.1 Prior Work . .2 1.2 Motivation . .4 1.3 Organization of Thesis . .6 2. Characterization of the UAV System . .8 2.1 UAV System Representation . .8 2.2 UAV Power Demand . 10 2.3 Energy Management Considerations . 12 vii 3. Dynamic Programming for UAV Energy Management . 13 3.1 Dynamic Programming . 13 3.2 Application of Dynamic Programming to UAV Energy Management 15 3.2.1 Algorithmic Implementation . 18 3.3 UAV System Model . 20 3.4 Results . 22 3.5 Conclusion . 24 4. Model Predictive Control for UAV Energy Management . 25 4.1 Model Predictive Control (MPC) . 25 4.2 Application of MPC to UAV Energy Management . 27 4.2.1 Algorithmic Development . 30 4.3 UAV System Model . 33 4.4 Results . 34 4.4.1 Energy Management for a 50cc Engine . 34 4.4.2 Energy Management for a 28cc Engine . 42 4.5 Conclusion . 54 5. Additional Algorithmic Considerations - MPC for UAV Energy Optimization 57 5.1 The Importance of Horizon Length within the MPC for UAV Energy Optimization . 58 5.2 MPC in the Presence of Load Uncertainty . 62 5.3 Integrating Renewable Generation . 71 5.4 Conclusion . 77 6. Conclusions and Future Work . 80 Bibliography . 85 viii LIST OF TABLES Table Page 4.1 Fuel Consumption for Four Test Cases of the 50cc Engine . 35 4.2 Parameter, Horizon Length, and Saved Solution Values for Test Cases 1-4 (50cc Engine) . 38 4.3 Fuel Consumption Comparisons for MPC Output and UAV Model Sim- ulation (50cc Engine) . 39 4.4 Fuel Consumption for Two Cases of No Charging (28cc Engine) . 43 4.5 Fuel Consumption and Battery Size Comparisons for 24 and 36 Hour Missions . 48 4.6 Fuel Consumption for MPC Output and UAV Model Simulation (28cc Engine) . 51 4.7 Fuel Consumption of MPC Output and UAV Model Simulation in the Case of an Extended Mission (28cc Engine) . 54 5.1 Solution Speeds per Iteration for Various Horizon Lengths . 67 5.2 Double Junction Gallium Arsenide PV Product Characteristics [1] . 73 ix LIST OF FIGURES Figure Page 1.1 The proposed technique, wherein an optimization algorithm provides control decisions U to a realistic UAV model, which implements the decisions in a real-world scenario including non-linear dynamics. Per- formance is interpreted by operators, who provide algorithm alterations H......................................5 2.1 A simplified, block diagram representation of the UAV system under study. 10 2.2 The predetermined load profile developed based on a known mission. This profile is used during the planning scope, i.e., determining the cor- rect sizing of components to ensure minimum fuel consumption while completing the entire mission duration. 11 3.1 Illustration of the possible paths the system can take under a fixed C-rate assumption, showing the transition from SOC1 to SOC2.... 18 3.2 UAV Simulink model developed to validate results of the algorithm according to dynamics within the operating system. 21 3.3 A comparison of the DP Formulation simulation and the Simulink UAV model simulation. 22 4.1 An illustration of the moving horizon of the MPC algorithm imple- mented. Predefined values of saved solutions m = 5; 000 and horizon length h = 10; 000 are used. The algorithm starts at time zero and solves for solutions up to the black line. 5; 000 solutions are then saved, up to the green line. The horizon then shifts forward by 10; 000 and solves from the green line to the blue line. This process repeats until the entire mission is solved. 31 x 4.2 The UAV Simulink model developed to validate results and for use within real-time implementation with the MPC formulation. This model considers the dynamics of the UAV. 33 4.3 Energy Source Output results of the MPC algorithm for four cases of UAV operation: (i) no charging needed for the duration of the mission, (ii) aggressive charging behavior, (iii) a gradual charge pattern, and (iv) linear charging to meet future dash demand. Battery power is shown in blue, load power is shown in yellow, and engine power is shown in red. 36 4.4 Battery SOC results of the MPC algorithm for four cases of UAV op- eration: (i) no charging needed for the duration of the mission, (ii) aggressive charging behavior, (iii) a more gradual charge pattern, and (iv) linear charging to meet future dash demand. 37 4.5 A comparison of algorithmic and UAV model results for two cases. The first case does not charge the battery, with battery power and SOC results shown in (a) and (b), respectively. The second case implements aggressive charging, with battery power shown in (c) and SOC shown in (d). 40 4.6 A comparison of algorithmic and UAV model results for two cases of charging. The first case follows a gradual charge strategy, with battery power and SOC results shown in (a) and (b), respectively. The second case implements linear charging, with battery power shown in (c) and SOC shown in (d). 41 4.7 Energy source output power (first row) and SOC (second row) results of the MPC algorithm for cases of no charging where (i) engine power is optimal and (ii) battery discharge equals zero during cruise periods.
Load more

Recommended publications
  • 25Th Space Photovoltaic Research and Technology (SPRAT XXV) Conference
    National Aeronautics and Space Administration An Overview of The Photovoltaic and Electrochemical Systems Branch at the NASA Glenn Research Center Eric Clark/NASA GRC 25th Space Photovoltaic Research and Technology (SPRAT XXV) Conference Ohio Aerospace Institute Cleveland, Ohio September 19, 2018 www.nasa.gov 1 National Aeronautics and Space Administration Outline • Introduction/History • Current Projects – Photovoltaics – Batteries – Fuel Cells • Future Technology Needs • Conclusions www.nasa.gov 2 National Aeronautics and Space Administration Introduction • The Photovoltaic and Electrochemical Systems Branch (LEX) at the NASA Glenn Research Center (GRC) supports a wide variety of space and aeronautics missions, through research, development, evaluation, and oversight. –Solar cells, thermal energy conversion, advanced array components, and novel array concepts –Low TRL R&D to component evaluation & flight experiments –Supports NASA missions through PV expertise and facilities –Management of SBIR/STTR Topics, Subtopics, and individual efforts. • LEX works closely with other NASA organizations, academic institutions, commercial partners, and other Government entities. www.nasa.gov 3 National Aeronautics and Space Administration Examples of LEX activities Advanced Solar Arrays Solar Cells Array Blanket and Component Technology Solar Cell Measurements & Calibration Solar Array Space Environmental Effects www.nasa.gov 4 National Aeronautics and Space Administration History • 1991The Photovoltaic Branch – Multijunction Cell development, Advanced
    [Show full text]
  • Mini Unmanned Aerial Systems (UAV) - a Review of the Parameters for Classification of a Mini AU V
    International Journal of Aviation, Aeronautics, and Aerospace Volume 7 Issue 3 Article 5 2020 Mini Unmanned Aerial Systems (UAV) - A Review of the Parameters for Classification of a Mini AU V. Ramesh PS Lovely Professional University, [email protected] Muruga Lal Jeyan Lovely professional university, [email protected] Follow this and additional works at: https://commons.erau.edu/ijaaa Part of the Aeronautical Vehicles Commons Scholarly Commons Citation PS, R., & Jeyan, M. L. (2020). Mini Unmanned Aerial Systems (UAV) - A Review of the Parameters for Classification of a Mini AU V.. International Journal of Aviation, Aeronautics, and Aerospace, 7(3). https://doi.org/10.15394/ijaaa.2020.1503 This Literature Review is brought to you for free and open access by the Journals at Scholarly Commons. It has been accepted for inclusion in International Journal of Aviation, Aeronautics, and Aerospace by an authorized administrator of Scholarly Commons. For more information, please contact [email protected]. PS and Jeyan: Parameters for Classification of a Mini UAV. The advent of Unmanned Aerial Vehicle (UAV) has redefined the battle space due to the ability to perform tasks which are categorised as dull, dirty, and dangerous. UAVs re-designated as Unmanned Aerial Systems (UAS) are now being developed to provide cost effective efficient solutions for specific applications, both in the spectrum of military and civilian usage. US Office of the Secretary of Defense (2013) describes UAS as a “system whose components include the necessary equipment, network, and personnel to control an unmanned aircraft.” In an earlier paper, US Office of the Secretary of Defense (2005) specifies UAV as the airborne element of the UAS and defines UAV as “A powered, aerial vehicle that does not carry a human operator, uses aerodynamic forces to provide vehicle lift, can fly autonomously or be piloted remotely, can be expendable or recoverable, and can carry a lethal or non-lethal payload.” John (2010) provided an excellent historical perspective about the evolution of the UAVs.
    [Show full text]
  • Introduction to Unmanned Aerial Vehicle (UAV) Flight
    Introduction to Unmanned Aerial Vehicle (UAV) Flight PEIMS Code: N1304670 Abbreviation: PRINUAV Grade Level(s): 10-12 Award of Credit: 1.0 Approved Innovative Course • Districts must have local board approval to implement innovative courses. • In accordance with Texas Administrative Code (TAC) §74.27, school districts must provide instruction in all essential knowledge and skills identified in this innovative course. • Innovative courses may only satisfy elective credit toward graduation requirements. • Please refer to TAC §74.13 for guidance on endorsements. Course Description: The Introduction to Unmanned Aerial Vehicle (UAV) Flight course is designed to prepare students for entry-level employment or continuing education in piloting UAV operations. Principles of UAV is designed to instruct students in UAV flight navigation, industry laws and regulations, and safety regulations. Students are also exposed to mission planning procedures, environmental factors, and human factors involved in the UAV industry. Essential Knowledge and Skills: (a) General Requirements. This course is recommended for students in Grades 10-12. Recommended prerequisite: Principles of Transportation Systems. Students shall be awarded one credit for successful completion of this course. (b) Introduction (1) The Transportation, Distribution, and Logistics Career Cluster focuses on planning, management, and movement of people, materials, and goods by road, pipeline, air, rail, and water and related professional support services such as transportation infrastructure planning and management, logistics services, mobile equipment, and facility maintenance. (2) The Introduction to Unmanned Aerial Vehicle (UAV) Flight course is designed to prepare students for entry-level employment or continuing education in piloting UAV operations. Principles of UAV is designed to instruct students in UAV flight navigation, industry laws and regulations, and safety regulations.
    [Show full text]
  • Design, Development, and Initial Testing of a Computationally-Intensive, Long-Endurance Solar-Powered Unmanned Aircraft
    Design, Development, and Initial Testing of a Computationally-Intensive, Long-Endurance Solar-Powered Unmanned Aircraft Or D. Dantsker,∗ Mirco Theile,† and Marco Caccamo‡ Renato Mancuso§ University of Illinois at Urbana–Champaign, Urbana, IL 61801 Boston University, Boston, MA 02215 In recent years, we have seen an uptrend in the popularity of UAVs driven by the desire to apply these aircraft to areas such as precision farming, infrastructure and environment monitoring, surveillance, surveying and mapping, search and rescue missions, weather forecasting, and more. The traditional approach for small size UAVs is to capture data on the aircraft, stream it to the ground through a high power data-link, process it remotely (potentially off-line), perform analysis, and then relay commands back to the aircraft as needed. All the mentioned application scenarios would benefit by carrying a high performance embedded computer system to minimize the need for data transmission. A major technical hurdle to overcome is that of drastically reducing the overall power consumption of these UAVs so that they can be powered by solar arrays. This paper describes the work done to date developing the 4.0 m (157 in) wingspan, UIUC Solar Flyer, which will be a long-endurance solar-powered unmanned aircraft capable of performing computationally-intensive on-board data processing. A mixture of aircraft requirements, trade studies, development work, and initial testing will be presented. Nomenclature CG = center of gravity DOF = degree of freedom ESC = electronic speed controller GPS = global navigation satellite system IMU = inertial measurement unit IR = infrared L/D = lift-to-drag ratio PW M = pulse width modulation RC = radio control AR = aspect ratio b = wingspan c = wing mean chord g = gravitational acceleration L = aircraft length m = aircraft mass P = power p, q, r = roll, pitch and yaw rates S = wing area W = weight v = velocity ∗Graduate Research Fellow, Department of Aerospace Engineering, AIAA Student Member.
    [Show full text]
  • Self Powered Electric Airplanes
    Advances in Aerospace Science and Applications. ISSN 2277-3223 Volume 3, Number 2 (2013), pp. 45-50 © Research India Publications http://www.ripublication.com/aasa.htm Self Powered Electric Airplanes Adesh Ramdas Nakashe 1and C. Lokesh2 1,2Department of Aeronautical Engineering Rajalakshmi Engineering College Chennai-602105, Tamil Nadu, India. Abstract The field of aeronautical engineering began to foresee its advancements in the future, the moment it evolved. Various new technologies and techniques were discovered and implemented almost in all branches of aviation industry. One branch where the researchers are continuously working for further more development is propulsion. Many new ideas are continuously being proposed. This paper deals with the use of renewable energy as the source of power for the aircraft. It gathers or creates the energy to move ON ITS OWN, it uses NO fuel. It is electric, having motors powered by electricity for propulsion. We are going to apply the same principle of electrical airplane and this can be operated as self powered electrical airplane. Here, starting power is provided to the engine and when engine gets maximum torque it starts generating current as per wind mill principle. As it produces electricity that will be used as the input for engine, so there is no need of any external electrical supply further. Efficiency of power produced can be increase to 100% by using electromagnetic generators. So the aircraft will be self driven and electrically powered. Keywords: Renewable energy, Self-powered, Electromagnetic generators. 1. Introduction This paper deals with the conceptual design of an electrically powered commercial aircraft that can carry 30 to 40 passengers.
    [Show full text]
  • Electrical Generation for More-Electric Aircraft Using Solid Oxide Fuel Cells
    PNNL-XXXXX Prepared for the U.S. Department of Energy under Contract DE-AC05-76RL01830 Electrical Generation for More-Electric Aircraft using Solid Oxide Fuel Cells GA Whyatt LA Chick April 2012 PNNL-XXXXX Electrical Generation for More- Electric Aircraft using Solid Oxide Fuel Cells GA Whyatt LA Chick April 2012 Prepared for the U.S. Department of Energy under Contract DE-AC05-76RL01830 Pacific Northwest National Laboratory Richland, Washington 99352 Summary This report examines the potential for Solid-Oxide Fuel Cells (SOFC) to provide electrical generation on-board commercial aircraft. Unlike a turbine-based auxiliary power unit (APU) a solid oxide fuel cell power unit (SOFCPU) would be more efficient than using the main engine generators to generate electricity and would operate continuously during flight. The focus of this study is on more-electric aircraft which minimize bleed air extraction from the engines and instead use electrical power obtained from generators driven by the main engines to satisfy all major loads. The increased electrical generation increases the potential fuel savings obtainable through more efficient electrical generation using a SOFCPU. However, the weight added to the aircraft by the SOFCPU impacts the main engine fuel consumption which reduces the potential fuel savings. To investigate these relationships the Boeing 787-8 was used as a case study. The potential performance of the SOFCPU was determined by coupling flowsheet modeling using ChemCAD software with a stack performance algorithm. For a given stack operating condition (cell voltage, anode utilization, stack pressure, target cell exit temperature), ChemCAD software was used to determine the cathode air rate to provide stack thermal balance, the heat exchanger duties, the gross power output for a given fuel rate, the parasitic power for the anode recycle blower and net power obtained from (or required by) the compressor/expander.
    [Show full text]
  • Electric Propulsion
    UNIVERSITY LEAD INITIATIVE Dr. Mike Benzakein Assistant Vice President, Aerospace and Aviation UNIVERSITY LED INITIATIVE Electric Propulsion – Challenges and Opportunities The challenges and the goals: • The team • System integration vehicle sizing • Batteries energy storage • Electric machines • Thermal management • The demonstration WHY ARE WE DOING THIS? • World population is growing 10 Billion by 2100 • Commercial airplanes will double in the next 20 years, causing increased CO2 emissions that affect health across the globe. • Goal is to have a carbon neutral environment by 2050. • National Academy of Engineering has established that a reduction of a 20% in fuel burn and CO2 could be attained with electric propulsion. Great to help the environment, but challenges remain THE TEAM System Integration Vehicle Sizing Initial Sizing Thermal Management Final Concept 1. Requirements Battery Definition Definition 2. Electric Power 1. Iterate with battery testing 1. Update Scaling Usage 2. Trade battery life against laws, and maps 3. density 2. Energy storage 4. 3. 3. July 2017 – July 2018 4. 4. Prelim. Sizing Resized vehicle Vehicle Design Frozen Vehicle Update July 2018 June 2019 June 2020 June 2021 Iterative cooperative process Vehicle Update between Universities June 2022 ULI Concept Benefits Assessment Baseline Aircraft Next Generation Distributed Hybrid (CRJ 900) Aircraft Turbo Electric 8% Distributed Propulsion 9% and typical payload and typical Use of Hybrid Propulsion 6% Fuel Burn Reduction at 600 nmi Reduction Burn Fuel 15% improvement
    [Show full text]
  • Optimal Control of a Helicopter Unmanned Aerial Vehicle (UAV)
    Scholars' Mine Masters Theses Student Theses and Dissertations 2011 Optimal control of a helicopter unmanned aerial vehicle (UAV) David John Nodland Follow this and additional works at: https://scholarsmine.mst.edu/masters_theses Part of the Electrical and Computer Engineering Commons Department: Recommended Citation Nodland, David John, "Optimal control of a helicopter unmanned aerial vehicle (UAV)" (2011). Masters Theses. 5417. https://scholarsmine.mst.edu/masters_theses/5417 This thesis is brought to you by Scholars' Mine, a service of the Missouri S&T Library and Learning Resources. This work is protected by U. S. Copyright Law. Unauthorized use including reproduction for redistribution requires the permission of the copyright holder. For more information, please contact [email protected]. i ii OPTIMAL CONTROL OF A HELICOPTER UNMANNED AERIAL VEHICLE (UAV) by DAVID JOHN NODLAND A THESIS Presented to the Faculty of the Graduate School of the MISSOURI UNIVERSITY OF SCIENCE AND TECHNOLOGY In Partial Fulfillment of the Requirements for the Degree MASTER OF SCIENCE IN ELECTRICAL ENGINEERING 2011 Approved by Jagannathan Sarangapani, Advisor Kelvin T. Erickson Maciej Zawodniok iii iii PUBLICATION THESIS OPTION This thesis consists of the following two papers: paper 1, pages 10-57, D. Nodland, H. Zargarzadeh, and S. Jagannathan, “Neural Network-based Optimal Output Feedback Control for Trajectory Tracking of a Helicopter UAV,” to be submitted to IEEE Transactions on Neural Networks, and paper 2, pages 58-91, D. Nodland, A. Ghosh, H. Zargarzadeh, and S. Jagannathan, “Neuro-Optimal Control of an Unmanned Helicopter,” in Journal of Defense Modeling and Simulation Special Issue: Intelligent Behaviors in Military Unmanned Systems, 2012, to appear.
    [Show full text]
  • Unmanned Aircraft System (UAS): Regulatory Framework and Challenges
    Unmanned Aircraft System (UAS): regulatory framework and challenges NAM/CAR/SAM Civil - Military Cooperation Havana, Cuba, 13 – 17 April 2015 Overview • Background • Objective • UAV? • Assumptions • Challenges • Regulatory Framework • UAS in ATM System • Emerging Situational Technologies • Recommendations Background Can an UAS operate in controlled airspace? Which technologies can be used to reduce the impact? UAS in civil applications Improve the regulations for UAS operations ICAO Global ATM operational concept (Doc 9854) UAV: “[a]n unmanned aerial vehicle is a pilotless aircraft, in the sense of Article 8 of the Convention on International Civil Aviation, which is flown without a pilot-in-command on-board and is either remotely and fully controlled from another place (ground, another aircraft, space) or programmed and fully autonomous.” Objective • This presentation provides an overview of the regulatory frameworks for the UAS activities and how to ensure safe operations in the ATS system. • It also addresses regional coordination between States and other stakeholders for UAS operations during natural disaster events. • It explains future challenges of the UAS into the ATM system. 6 Assumptions UAS is another user of the airspace The ATM should be able to allow the UAS operations The activities should include both civil and military air operations The first step is regulatory framework for the UAS in order to ensure safety integrated operations into the ATM system States to disseminate ATS procedures for UAS air operations UAVs applications Demand of RPAS for Military & civil operations International Military operations SAR, Coastguard / coastline and sea-lane monitoring Fire Services and Forestry Fire detection, incident control Owners / operators of model aircraft doing to commercial activity Many non-aviation businesses and entities importing RPAS Aerial photography, Film, video, still, etc.
    [Show full text]
  • Unmanned Aerial Vehicle Operating Mode Classification Using Deep
    aerospace Article Unmanned Aerial Vehicle Operating Mode Classification Using Deep Residual Learning Feature Extraction Carolyn J. Swinney 1,2,* and John C. Woods 1 1 Computer Science and Electronic Engineering Department, University of Essex, Colchester CO4 3SQ, UK; [email protected] 2 Air and Space Warfare Centre, Royal Air Force Waddington, Lincoln LN5 9NB, UK * Correspondence: [email protected] Abstract: Unmanned Aerial Vehicles (UAVs) undoubtedly pose many security challenges. We need only look to the December 2018 Gatwick Airport incident for an example of the disruption UAVs can cause. In total, 1000 flights were grounded for 36 h over the Christmas period which was estimated to cost over 50 million pounds. In this paper, we introduce a novel approach which considers UAV detection as an imagery classification problem. We consider signal representations Power Spectral Density (PSD); Spectrogram, Histogram and raw IQ constellation as graphical images presented to a deep Convolution Neural Network (CNN) ResNet50 for feature extraction. Pre-trained on ImageNet, transfer learning is utilised to mitigate the requirement for a large signal dataset. We evaluate performance through machine learning classifier Logistic Regression. Three popular UAVs are classified in different modes; switched on; hovering; flying; flying with video; and no UAV present, creating a total of 10 classes. Our results, validated with 5-fold cross validation and an independent dataset, show PSD representation to produce over 91% accuracy for 10 classifications. Our paper treats UAV detection as an imagery classification problem by presenting signal representations as images to a ResNet50, utilising the benefits of transfer learning and outperforming previous work in the field.
    [Show full text]
  • Downloadfile/566729524649660/Duartefigueiredo Thesis.Pdf (Accessed on 20 May 2021)
    drones Article Development of a Solar-Powered Unmanned Aerial Vehicle for Extended Flight Endurance Yauhei Chu †, Chunleung Ho †, Yoonjo Lee † and Boyang Li * Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China; [email protected] (Y.C.); [email protected] (C.H.); [email protected] (Y.L.) * Correspondence: [email protected]; Tel.: +852-340-082-31 † Authors have contributed equally. Abstract: Having an exciting array of applications, the scope of unmanned aerial vehicle (UAV) application could be far wider one if its flight endurance can be prolonged. Solar-powered UAV, promising notable prolongation in flight endurance, is drawing increasing attention in the industries’ recent research and development. This work arose from a Bachelor’s degree capstone project at Hong Kong Polytechnic University. The project aims to modify a 2-metre wingspan remote-controlled (RC) UAV available in the consumer market to be powered by a combination of solar and battery-stored power. The major objective is to greatly increase the flight endurance of the UAV by the power generated from the solar panels. The power system is first designed by selecting the suitable system architecture and then by selecting suitable components related to solar power. The flight control system is configured to conduct flight tests and validate the power system performance. Under fair experimental conditions with desirable weather conditions, the solar power system on the aircraft results in 22.5% savings in the use of battery-stored capacity. The decrease rate of battery voltage Citation: Chu, Y.; Ho, C.; Lee, Y.; Li, during the stable level flight of the solar-powered UAV built is also much slower than the same B.
    [Show full text]
  • Integration of Photovoltaic Cells Into Composite Wing Skins
    INTEGRATION OF PHOTOVOLTAIC CELLS INTO COMPOSITE WING SKINS By JAMES LEONARD Bachelor of Science in Aerospace and Mechanical Engineering Oklahoma State University Stillwater, Oklahoma 2010 Submitted to the Faculty of the Graduate College of the Oklahoma State University in partial fulfillment of the requirements for the Degree of MASTER OF SCIENCE December, 2014 INTEGRATION OF PHOTOVOLTAIC CELLS INTO COMPOSITE WING SKINS Thesis Approved: Dr. Jamey Jacob Thesis Adviser Dr. Andy Arena Dr. Joe Conner ii Name: JAMES LEONARD Date of Degree: DECEMBER, 2014 Title of Study: INTEGRATION OF PHOTOVOLTAIC CELLS INTO COMPOSITE WING SKINS Major Field: MECHANICAL AND AEROSPACE ENGINEERING ABSTRACT: The integration of thin film solar cells into composite wing skins is explored by first testing and evaluating the integration of single solar cells into small composite samples with no encapsulating material, fiberglass encapsulating material and polyurethane film encapsulating material for the impacts that these processes and materials have on solar cell performance, aircraft performance and solar cell durability. Moving on from single cell samples, three encapsulation methods were chosen to be used in the construction of two wings utilizing arrays of multiple solar cells with each encapsulation method being utilized on 3 of the four wing skins comprising the 2 complete wings. The fourth wing skin was integrated with a functioning removable solar panel manufactured to the contours of the wing. Performance and weight data gathered from the development and fabrication of single cell and wing-skin specimens was used to develop a basic model of endurance for each encapsulation material evaluated in order to compare the effects of encapsulation materials and processes on the primary parameter that the integration of the photovoltaic cells into the wing skins is intended to improve.
    [Show full text]