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Gokhan Inalhan received his B.Sc. degree in Aeronautical Engineering from Istanbul Technical University in 1997, and M.Sc. and Ph.D. degrees in Aeronautics and Astronautics from Stanford University in 1998 and 2004, respectively. In 2003, he received a Ph.D. Minor from Stanford University in Engineering Economics and Operations Research (currently, Management Science and Engineering). Between 2004 and 2006, he worked as a Postdoctoral Associate at Massachusetts Institute of Technology. During this period, he led the Communication and Navigation group in the MIT-Draper Laboratory NASA CER project. He has served as the Director-General of the Aerospace Research Centre (2016-2019) at Istanbul Technical University. Gokhan is currently the BAE Systems Chair, Professor of Autonomous Systems and Artificial Intelligence and Deputy Head of the Centre for Autonomous and Cyber-Physical Systems at Cranfield University. He and his research group focus on design, modeling, GNC, resilience, and security aspects of autonomy and artificial intelligence for air, defense, transportation and space systems.
In this study, reinforcement learning (RL)-based centralized path planning is performed for an unmanned combat aerial vehicle (UCAV) fleet in a human-made hostile environment. The proposed method provides a novel approach in which closing speed and approximate time-to-go terms are used in the reward function to obtain cooperative motion while ensuring no-fly-zones (NFZs) and time-of-arrival constraints. Proximal policy optimization (PPO) algorithm is used in the training phase of the RL agent. System performance is evaluated in two different cases. In case 1, the warfare environment contains only the target area, and simultaneous arrival is desired to obtain the saturated attack effect. In case 2, the warfare environment contains NFZs in addition to the target area and the standard saturated attack and collision avoidance requirements. Particle swarm optimization (PSO)-based cooperative path planning algorithm is implemented as the baseline method, and it is compared with the proposed algorithm in terms of execution time and developed performance metrics. Monte Carlo simulation studies are performed to evaluate the system performance. According to the simulation results, the proposed system is able to generate feasible flight paths in real-time while considering the physical and operational constraints such as acceleration limits, NFZ restrictions, simultaneous arrival, and collision avoidance requirements. In that respect, the approach provides a novel and computationally efficient method for solving the large-scale cooperative path planning for UCAV fleets.
Burak Yuksek; Mustafa Umut Demirezen; Gokhan Inalhan; Antonios Tsourdos. Cooperative Planning for an Unmanned Combat Aerial Vehicle Fleet Using Reinforcement Learning. Journal of Aerospace Information Systems 2021, 1 -12.
AMA StyleBurak Yuksek, Mustafa Umut Demirezen, Gokhan Inalhan, Antonios Tsourdos. Cooperative Planning for an Unmanned Combat Aerial Vehicle Fleet Using Reinforcement Learning. Journal of Aerospace Information Systems. 2021; ():1-12.
Chicago/Turabian StyleBurak Yuksek; Mustafa Umut Demirezen; Gokhan Inalhan; Antonios Tsourdos. 2021. "Cooperative Planning for an Unmanned Combat Aerial Vehicle Fleet Using Reinforcement Learning." Journal of Aerospace Information Systems , no. : 1-12.
The growth of the Internet of Things (IoT) offers numerous opportunities for developing industrial applications such as smart grids, smart cities, smart manufacturers, etc. By utilising these opportunities, businesses engage in creating the Industrial Internet of Things (IIoT). IoT is vulnerable to hacks and, therefore, requires various techniques to achieve the level of security required. Furthermore, the wider implementation of IIoT causes an even greater security risk than its benefits. To provide a roadmap for researchers, this survey discusses the integrity of industrial IoT systems and highlights the existing security approaches for the most significant industrial applications. This paper mainly classifies the attacks and possible security solutions regarding IoT layers architecture. Consequently, each attack is connected to one or more layers of the architecture accompanied by a literature analysis on the various IoT security countermeasures. It further provides a critical analysis of the existing IoT/IIoT solutions based on different security mechanisms, including communications protocols, networking, cryptography and intrusion detection systems. Additionally, there is a discussion of the emerging tools and simulations used for testing and evaluating security mechanisms in IoT applications. Last, this survey outlines several other relevant research issues and challenges for IoT/IIoT security.
Nasr Abosata; Saba Al-Rubaye; Gokhan Inalhan; Christos Emmanouilidis. Internet of Things for System Integrity: A Comprehensive Survey on Security, Attacks and Countermeasures for Industrial Applications. Sensors 2021, 21, 3654 .
AMA StyleNasr Abosata, Saba Al-Rubaye, Gokhan Inalhan, Christos Emmanouilidis. Internet of Things for System Integrity: A Comprehensive Survey on Security, Attacks and Countermeasures for Industrial Applications. Sensors. 2021; 21 (11):3654.
Chicago/Turabian StyleNasr Abosata; Saba Al-Rubaye; Gokhan Inalhan; Christos Emmanouilidis. 2021. "Internet of Things for System Integrity: A Comprehensive Survey on Security, Attacks and Countermeasures for Industrial Applications." Sensors 21, no. 11: 3654.
This paper presents a physics-guided deep neural network framework to estimate fuel consumption of an aircraft. The framework aims to improve data-driven models’ consistency in flight regimes that are not covered by data. In particular, we guide the neural network with the equations that represent fuel flow dynamics. In addition to the empirical error, we embed this physical knowledge as several extra loss terms. Results show that our proposed model accomplishes correct predictions on the labeled test set, as well as assuring physical consistency in unseen flight regimes. The results indicate that our model, while being applicable to the aircraft’s complete flight envelope, yields lower fuel consumption error measures compared to the model-based approaches and other supervised learning techniques utilizing the same training data sets. In addition, our deep learning model produces fuel consumption trends similar to the BADA4 aircraft performance model, which is widely utilized in real-world operations, in unseen and untrained flight regimes. In contrast, the other supervised learning techniques fail to produce meaningful results. Overall, the proposed methodology enhances the explainability of data-driven models without deteriorating accuracy.
Mevlut Uzun; Mustafa Demirezen; Gokhan Inalhan. Physics Guided Deep Learning for Data-Driven Aircraft Fuel Consumption Modeling. Aerospace 2021, 8, 44 .
AMA StyleMevlut Uzun, Mustafa Demirezen, Gokhan Inalhan. Physics Guided Deep Learning for Data-Driven Aircraft Fuel Consumption Modeling. Aerospace. 2021; 8 (2):44.
Chicago/Turabian StyleMevlut Uzun; Mustafa Demirezen; Gokhan Inalhan. 2021. "Physics Guided Deep Learning for Data-Driven Aircraft Fuel Consumption Modeling." Aerospace 8, no. 2: 44.
Hasan Karali; Umut M. Demirezen; Mahmut A. Yukselen; Gokhan Inalhan. A Novel Physics Informed Deep Learning Method for Simulation-Based Modelling. AIAA Scitech 2021 Forum 2021, 1 .
AMA StyleHasan Karali, Umut M. Demirezen, Mahmut A. Yukselen, Gokhan Inalhan. A Novel Physics Informed Deep Learning Method for Simulation-Based Modelling. AIAA Scitech 2021 Forum. 2021; ():1.
Chicago/Turabian StyleHasan Karali; Umut M. Demirezen; Mahmut A. Yukselen; Gokhan Inalhan. 2021. "A Novel Physics Informed Deep Learning Method for Simulation-Based Modelling." AIAA Scitech 2021 Forum , no. : 1.
Burak Yuksek; Umut M. Demirezen; Gokhan Inalhan. Development of UCAV Fleet Autonomy by Reinforcement Learning in a Wargame Simulation Environment. AIAA Scitech 2021 Forum 2021, 1 .
AMA StyleBurak Yuksek, Umut M. Demirezen, Gokhan Inalhan. Development of UCAV Fleet Autonomy by Reinforcement Learning in a Wargame Simulation Environment. AIAA Scitech 2021 Forum. 2021; ():1.
Chicago/Turabian StyleBurak Yuksek; Umut M. Demirezen; Gokhan Inalhan. 2021. "Development of UCAV Fleet Autonomy by Reinforcement Learning in a Wargame Simulation Environment." AIAA Scitech 2021 Forum , no. : 1.
Harry M. Lyon; Gokhan Inalhan; Daniel Bourne; Antonios Tsourdos. High-Altitude UAS Pseudo-Satellites: Architecture for End-to-End Military Communications. AIAA Scitech 2021 Forum 2021, 1 .
AMA StyleHarry M. Lyon, Gokhan Inalhan, Daniel Bourne, Antonios Tsourdos. High-Altitude UAS Pseudo-Satellites: Architecture for End-to-End Military Communications. AIAA Scitech 2021 Forum. 2021; ():1.
Chicago/Turabian StyleHarry M. Lyon; Gokhan Inalhan; Daniel Bourne; Antonios Tsourdos. 2021. "High-Altitude UAS Pseudo-Satellites: Architecture for End-to-End Military Communications." AIAA Scitech 2021 Forum , no. : 1.
In this work, a computationally efficient and high-precision nonlinear aerodynamic configuration analysis method is presented for both design optimization and mathematical modeling of small unmanned aerial vehicles. First, we have developed a novel nonlinear lifting line method which (a) provides very good match for the pre- and post-stall aerodynamic behavior in comparison to experiments and computationally intensive tools, (b) generates these results in order of magnitudes less time in comparison to computationally intensive methods such as computational fluid dynamics. This method is further extended to a complete configuration analysis tool that incorporates the effects of basic fuselage geometries. Moreover, a deep learning based surrogate model is developed using data generated by the new aerodynamic tool that can characterize the nonlinear aerodynamic performance of unmanned aerial vehicles. The major novel feature of this model is that it can predict the aerodynamic properties of unmanned aerial vehicle configurations by using only geometric parameters without the need for any special input data or pre-process phase as needed by other computational aerodynamic analysis tools. The obtained black-box function can calculate the performance of an unmanned aerial vehicle over a wide angle of attack range on the order of milliseconds, whereas computational fluid dynamics solutions take several days/weeks in a similar computational environment. The aerodynamic model predictions show an almost 1-1 coincidence with the numerical data even for configurations with different airfoils that are not used in model training. The developed model provides a highly capable aerodynamic solver for design optimization studies as demonstrated through an illustrative profile design example.
Hasan Karali; Gokhan Inalhan; M Umut Demirezen; M Adil Yukselen. A new nonlinear lifting line method for aerodynamic analysis and deep learning modeling of small unmanned aerial vehicles. International Journal of Micro Air Vehicles 2021, 13, 1 .
AMA StyleHasan Karali, Gokhan Inalhan, M Umut Demirezen, M Adil Yukselen. A new nonlinear lifting line method for aerodynamic analysis and deep learning modeling of small unmanned aerial vehicles. International Journal of Micro Air Vehicles. 2021; 13 ():1.
Chicago/Turabian StyleHasan Karali; Gokhan Inalhan; M Umut Demirezen; M Adil Yukselen. 2021. "A new nonlinear lifting line method for aerodynamic analysis and deep learning modeling of small unmanned aerial vehicles." International Journal of Micro Air Vehicles 13, no. : 1.
In this study, we present a reinforcement learning (RL)‐based flight control system design method to improve the transient response performance of a closed‐loop reference model (CRM) adaptive control system. The methodology, known as RL‐CRM, relies on the generation of a dynamic adaption strategy by implementing RL on the variable factor in the feedback path gain matrix of the reference model. An actor‐critic RL agent is designed using the performance‐driven reward functions and tracking error observations from the environment. In the training phase, a deep deterministic policy gradient algorithm is utilized to learn the time‐varying adaptation strategy of the design parameter in the reference model feedback gain matrix. The proposed control structure provides the possibility to learn numerous adaptation strategies across a wide range of flight and vehicle conditions instead of being driven by high‐fidelity simulators or flight testing and real flight operations. The performance of the proposed system was evaluated on an identified and verified mathematical model of an agile quadrotor platform. Monte‐Carlo simulations and worst case analysis were also performed over a benchmark helicopter example model. In comparison to the classical model reference adaptive control and CRM‐adaptive control system designs, the proposed RL‐CRM adaptive flight control system design improves the transient response performance on all associated metrics and provides the capability to operate over a wide range of parametric uncertainties.
Burak Yuksek; Gokhan Inalhan. Reinforcement learning based closed‐loop reference model adaptive flight control system design. International Journal of Adaptive Control and Signal Processing 2020, 35, 420 -440.
AMA StyleBurak Yuksek, Gokhan Inalhan. Reinforcement learning based closed‐loop reference model adaptive flight control system design. International Journal of Adaptive Control and Signal Processing. 2020; 35 (3):420-440.
Chicago/Turabian StyleBurak Yuksek; Gokhan Inalhan. 2020. "Reinforcement learning based closed‐loop reference model adaptive flight control system design." International Journal of Adaptive Control and Signal Processing 35, no. 3: 420-440.
In this paper, we present a deep learning based surrogate model to determine non-linear aerodynamic characteristics of UAVs. The main advantage of this model is that it can predict the aerodynamic properties of the configurations very quickly by using only geometric configuration parameters without the need for any special input data or pre-process phase. This provides a crucial and explicit design and synthesis tool for mini and small UAVs. To achieve this goal, a large data set, which includes thousands of wing-tail configurations geometry parameters and performance coefficients, was generated using the previously developed and computationally very efficient non-linear lifting line method. This data is used for training the artificial neural network model. The preliminary results show that the neural network model has generalization capability. The aerodynamic model predictions show almost 1-1 coincidence with the numerical data even for configurations with different 2D profiles that are not used in model training. Specifically, the results of test cases are found to capture both the linear and non-linear region of the lift curves, by predicting the maximum lift coefficient, the stall angle of attack, and the characteristics of post-stall region correctly. Similarly, total drag and pitching moment coefficients are predicted successfully. The developed methodology provides the basis for bidirectional design optimization and offers insight for an inverse tool that can calculate geometry parameters for a given design condition.
Hasan Karali; Mustafa U. Demirezen; Mahmut A. Yukselen; Gokhan Inalhan. Design of a Deep Learning Based Nonlinear Aerodynamic Surrogate Model for UAVs. AIAA Scitech 2020 Forum 2020, 1 .
AMA StyleHasan Karali, Mustafa U. Demirezen, Mahmut A. Yukselen, Gokhan Inalhan. Design of a Deep Learning Based Nonlinear Aerodynamic Surrogate Model for UAVs. AIAA Scitech 2020 Forum. 2020; ():1.
Chicago/Turabian StyleHasan Karali; Mustafa U. Demirezen; Mahmut A. Yukselen; Gokhan Inalhan. 2020. "Design of a Deep Learning Based Nonlinear Aerodynamic Surrogate Model for UAVs." AIAA Scitech 2020 Forum , no. : 1.
In this work, we present a high fidelity model based progressive reinforcement learning method for control system design for an agile maneuvering UAV. Our work relies on a simulation-based training and testing environment for doing software-in-the-loop (SIL), hardware-in-the-loop (HIL) and integrated flight testing within photo-realistic virtual reality (VR) environment. Through progressive learning with the high fidelity agent and environment models, the guidance and control policies build agile maneuvering based on fundamental control laws. First, we provide insight on development of high fidelity mathematical models using frequency domain system identification. These models are later used to design reinforcement learning based adaptive flight control laws allowing the vehicle to be controlled over a wide range of operating conditions covering model changes on operating conditions such as payload, voltage and damage to actuators and electronic speed controllers (ESCs). We later design outer flight guidance and control laws. Our current work and progress is summarized in this work.
Can Bekar; Burak Yuksek; Gokhan Inalhan. High Fidelity Progressive Reinforcement Learning for Agile Maneuvering UAVs. AIAA Scitech 2020 Forum 2020, 1 .
AMA StyleCan Bekar, Burak Yuksek, Gokhan Inalhan. High Fidelity Progressive Reinforcement Learning for Agile Maneuvering UAVs. AIAA Scitech 2020 Forum. 2020; ():1.
Chicago/Turabian StyleCan Bekar; Burak Yuksek; Gokhan Inalhan. 2020. "High Fidelity Progressive Reinforcement Learning for Agile Maneuvering UAVs." AIAA Scitech 2020 Forum , no. : 1.
In this paper, we provide a system identification, model stitching and model-based flight control system design methodology for an agile maneuvering quadrotor micro aerial vehicle (MAV) technology demonstrator platform. The proposed MAV is designed to perform agile maneuvers in hover/low-speed and fast forward flight conditions in which significant changes in system dynamics are observed. As such, these significant changes result in considerable loss of performance and precision using classical hover or forward flight model based controller designs. To capture the changing dynamics, we consider an approach which is adapted from the full-scale manned aircraft and rotorcraft domain. Specifically, linear mathematical models of the MAV in hover and forward flight are obtained by using the frequency-domain system identification method and they are validated in time-domain. These point models are stitched with the trim data and quasi-nonlinear mathematical model is generated for simulation purposes. Identified linear models are used in a multi objective optimization based flight control system design approach in which several handling quality specifications are used to optimize the controller parameters. Lateral reposition and longitudinal depart/abort mission task elements from ADS-33E-PRF are scaled-down by using kinematic scaling to evaluate the proposed flight control systems. Position hold, trajectory tracking and aggressiveness analysis are performed, Monte-Carlo simulations and actual flight test results are compared. The results show that the proposed methodology provides high precision and predictable maneuvering control capability over an extensive speed envelope in comparison to classical control techniques. Our current work focuses on i) extension of the flight envelope of the mathematical model and ii) improvement of agile maneuvering capability of the MAV.
Burak Yuksek; Emre Saldiran; Aykut Cetin; Ramazan Yeniceri; Gokhan Inalhan. System Identification and Model-Based Flight Control System Design for an Agile Maneuvering Quadrotor Platform. AIAA Scitech 2020 Forum 2020, 1 .
AMA StyleBurak Yuksek, Emre Saldiran, Aykut Cetin, Ramazan Yeniceri, Gokhan Inalhan. System Identification and Model-Based Flight Control System Design for an Agile Maneuvering Quadrotor Platform. AIAA Scitech 2020 Forum. 2020; ():1.
Chicago/Turabian StyleBurak Yuksek; Emre Saldiran; Aykut Cetin; Ramazan Yeniceri; Gokhan Inalhan. 2020. "System Identification and Model-Based Flight Control System Design for an Agile Maneuvering Quadrotor Platform." AIAA Scitech 2020 Forum , no. : 1.
In this work, we provide insight on the avionics architecture design process for a future generation fighter program. This program carries a unique development phasing strategy which includes the design of the Block-0 prototype in 2026, the design of the Block-l in 2028 and full scale production block in 2030. Given this cascaded and progressive schedule, we consider an avionics architecture design process which can be further enhanced even during the development phase. However, all the design, integration, verification and long term modification activities of the fighter program are tightly coupled to the avionics architectural selections. In that sense, one the major architectural design drivers are to establish a design strategy and program flowchart to prevent extensive re-testing and re-certification issues between the different blocks of the program. In light of these constraints and schedule and cost risks, we consider a phase by phase progressive architecture development which can provide a basis for risk reduction through demonstrations, analyses, and modeling efforts. In that sense, we first review a) similar type of fighter aircrafts' avionics architectures and b) potential new technologies that will be available within the development cycle. Specifically, we highlight the main challenges that the system engineer must consider from technical, technology window, technology readiness, export licence and industrialization perspective. Specifically, for each of the blocks, different types of avionics architectures ranging from federated architecture to Integrated Modular Avionics (IMA) and Open System Architecture (OSA) are analyzed. In this work, we provide in detail the current avionics architecture for Block-0 Initial Operational Capability (IOC) which utilizes mainly Commercial off the Shelf (COTS) equipment and systems.
Z. Seda Mor; Naveed Asghar; Gokhan Inalhan. Avionics Architecture Design for a Future Generation Fighter Aircraft. 2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC) 2019, 1 -10.
AMA StyleZ. Seda Mor, Naveed Asghar, Gokhan Inalhan. Avionics Architecture Design for a Future Generation Fighter Aircraft. 2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC). 2019; ():1-10.
Chicago/Turabian StyleZ. Seda Mor; Naveed Asghar; Gokhan Inalhan. 2019. "Avionics Architecture Design for a Future Generation Fighter Aircraft." 2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC) , no. : 1-10.
This paper applies machine learning techniques to improve flight efficiency. Specifically, we focus on two distinct problems: uncertainties in aircraft performance models and uncertainties in wind. In this sense, this paper proposed methodologies to improve baseline models for fuel flow and wind estimations are via operational data. We utilize Base of Aircraft Data (BADA) 4 as baseline for aircraft performance model. Historical Global Forecast System (GFS) predictions are utilized as baseline estimations for $u$ and $v$ components of wind. As for the operational data, Quick Access Recorder (QAR) trajectory footprints of a narrow body and a wide body aircraft, which include actual recorded fuel flow from engines and measured wind speed and direction, are used. State-of-the-art deep learning algorithms are deployed to map baseline estimations for fuel flow and wind to their ground truths. Proper input parameters to have the best estimation results and be compatible with the ground-based flight planning systems are derived through extensive feature engineering. Comparison of the aircraft performance models with real flight data shows that precise estimation of fuel flow with mean absolute errors on a range of %0.1 - %0.7 can be achieved across all the flight modes. Results also show that we can achieve considerable reduction in wind uncertainty both from a mean error and variance sense. For short haul flights, the standard deviations of forecast errors in u and v components are reduced from 6.25 and 8.38 knots to 1.37 and 1.81 knots, respectively. The same reduction is from 11.02 and 10.89 knots to 4.88 and 4.76 knots in the long haul flights.
Mevlut Uzun; M. Umut Demirezen; Emre Koyuncu; Gokhan Inalhan; Javier Lopez; Miguel Vilaplana. Deep Learning Techniques for Improving Estimations of Key Parameters for Efficient Flight Planning. 2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC) 2019, 1 -8.
AMA StyleMevlut Uzun, M. Umut Demirezen, Emre Koyuncu, Gokhan Inalhan, Javier Lopez, Miguel Vilaplana. Deep Learning Techniques for Improving Estimations of Key Parameters for Efficient Flight Planning. 2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC). 2019; ():1-8.
Chicago/Turabian StyleMevlut Uzun; M. Umut Demirezen; Emre Koyuncu; Gokhan Inalhan; Javier Lopez; Miguel Vilaplana. 2019. "Deep Learning Techniques for Improving Estimations of Key Parameters for Efficient Flight Planning." 2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC) , no. : 1-8.
Samet Uzun; Berkay Akbiyik; Burak Yuksek; Umut Demirezen; Gokhan Inalhan. Correction: A Simulation-Based Machine Learning Approach for Flight Control System Design of Agile Maneuvering Multicopters. AIAA Scitech 2019 Forum 2019, 1 .
AMA StyleSamet Uzun, Berkay Akbiyik, Burak Yuksek, Umut Demirezen, Gokhan Inalhan. Correction: A Simulation-Based Machine Learning Approach for Flight Control System Design of Agile Maneuvering Multicopters. AIAA Scitech 2019 Forum. 2019; ():1.
Chicago/Turabian StyleSamet Uzun; Berkay Akbiyik; Burak Yuksek; Umut Demirezen; Gokhan Inalhan. 2019. "Correction: A Simulation-Based Machine Learning Approach for Flight Control System Design of Agile Maneuvering Multicopters." AIAA Scitech 2019 Forum , no. : 1.
Burak Yuksek; Emre Saldiran; Aykut Cetin; Ramazan Yeniceri; Gokhan Inalhan. A Model Based Flight Control System Design Approach for Micro Aerial Vehicle Using Integrated Flight Testing and HIL Simulation. AIAA Scitech 2019 Forum 2019, 1 .
AMA StyleBurak Yuksek, Emre Saldiran, Aykut Cetin, Ramazan Yeniceri, Gokhan Inalhan. A Model Based Flight Control System Design Approach for Micro Aerial Vehicle Using Integrated Flight Testing and HIL Simulation. AIAA Scitech 2019 Forum. 2019; ():1.
Chicago/Turabian StyleBurak Yuksek; Emre Saldiran; Aykut Cetin; Ramazan Yeniceri; Gokhan Inalhan. 2019. "A Model Based Flight Control System Design Approach for Micro Aerial Vehicle Using Integrated Flight Testing and HIL Simulation." AIAA Scitech 2019 Forum , no. : 1.
Hasan Karali; M. Adil Yükselen; Gokhan Inalhan. A New Non-Linear Lifting Line Method for 3D Analysis of Wing / Configuration Aerodynamic Characteristics with Application to UAVs. AIAA Scitech 2019 Forum 2019, 1 .
AMA StyleHasan Karali, M. Adil Yükselen, Gokhan Inalhan. A New Non-Linear Lifting Line Method for 3D Analysis of Wing / Configuration Aerodynamic Characteristics with Application to UAVs. AIAA Scitech 2019 Forum. 2019; ():1.
Chicago/Turabian StyleHasan Karali; M. Adil Yükselen; Gokhan Inalhan. 2019. "A New Non-Linear Lifting Line Method for 3D Analysis of Wing / Configuration Aerodynamic Characteristics with Application to UAVs." AIAA Scitech 2019 Forum , no. : 1.
Mehmet Akcakoca; Bilge Mirac Atici; Basak Gever; Sinan Oguz; Umut Demirezen; Mustafa Demir; Emre Saldiran; Burak Yuksek; Emre Koyuncu; Ramazan Yeniceri; Gokhan Inalhan. A Simulation-Based Development and Verification Architecture for Micro UAV Teams and Swarms. AIAA Scitech 2019 Forum 2019, 1 .
AMA StyleMehmet Akcakoca, Bilge Mirac Atici, Basak Gever, Sinan Oguz, Umut Demirezen, Mustafa Demir, Emre Saldiran, Burak Yuksek, Emre Koyuncu, Ramazan Yeniceri, Gokhan Inalhan. A Simulation-Based Development and Verification Architecture for Micro UAV Teams and Swarms. AIAA Scitech 2019 Forum. 2019; ():1.
Chicago/Turabian StyleMehmet Akcakoca; Bilge Mirac Atici; Basak Gever; Sinan Oguz; Umut Demirezen; Mustafa Demir; Emre Saldiran; Burak Yuksek; Emre Koyuncu; Ramazan Yeniceri; Gokhan Inalhan. 2019. "A Simulation-Based Development and Verification Architecture for Micro UAV Teams and Swarms." AIAA Scitech 2019 Forum , no. : 1.
Samet Uzun; Berkay Akbiyik; Burak Yuksek; Umut Demirezen; Gokhan Inalhan. A Simulation-Based Machine Learning Approach for Flight Control System Design of Agile Maneuvering Multicopters. AIAA Scitech 2019 Forum 2019, 1 .
AMA StyleSamet Uzun, Berkay Akbiyik, Burak Yuksek, Umut Demirezen, Gokhan Inalhan. A Simulation-Based Machine Learning Approach for Flight Control System Design of Agile Maneuvering Multicopters. AIAA Scitech 2019 Forum. 2019; ():1.
Chicago/Turabian StyleSamet Uzun; Berkay Akbiyik; Burak Yuksek; Umut Demirezen; Gokhan Inalhan. 2019. "A Simulation-Based Machine Learning Approach for Flight Control System Design of Agile Maneuvering Multicopters." AIAA Scitech 2019 Forum , no. : 1.
Omer Herekoglu; Mehmet Hasanzade; Emre Saldiran; Aykut Cetin; Irem Ozgur; Abdulkadir G. Kucukoglu; Mehmet Burak Üstün; Burak Yuksek; Ramazan Yeniceri; Emre Koyuncu; Gokhan Inalhan. Flight Testing of a Multiple UAV RF Emission and Vision Based Target Localization Method. AIAA Scitech 2019 Forum 2019, 1 .
AMA StyleOmer Herekoglu, Mehmet Hasanzade, Emre Saldiran, Aykut Cetin, Irem Ozgur, Abdulkadir G. Kucukoglu, Mehmet Burak Üstün, Burak Yuksek, Ramazan Yeniceri, Emre Koyuncu, Gokhan Inalhan. Flight Testing of a Multiple UAV RF Emission and Vision Based Target Localization Method. AIAA Scitech 2019 Forum. 2019; ():1.
Chicago/Turabian StyleOmer Herekoglu; Mehmet Hasanzade; Emre Saldiran; Aykut Cetin; Irem Ozgur; Abdulkadir G. Kucukoglu; Mehmet Burak Üstün; Burak Yuksek; Ramazan Yeniceri; Emre Koyuncu; Gokhan Inalhan. 2019. "Flight Testing of a Multiple UAV RF Emission and Vision Based Target Localization Method." AIAA Scitech 2019 Forum , no. : 1.
In the aspect of Trajectory Based Operations (TBO), accurate trajectory predictions are essential for meeting future concepts defined in SESAR and NextGen visions. The 4D trajectories are results of generated flight plans by Airline Operation Centers to be executed by pilots and monitored by Air Traffic Controllers. Trajectory generation is a process with many input sources such as flight route, speed schedule, initial conditions and environmental conditions. However, the uncertainty in these states generates discrepancies between planned and actual trajectories. This paper distinctly investigates the wind uncertainty by analyzing the forecast data in airline’s operational flight plans and actual wind computed by aircraft’s onboard systems (i.e., Quick Access Recorder). The study goes through the methodology of speed and altitude optimization for cruise phase. The simulation studies reveal the impact of wind uncertainty and potential fuel saving benefits when the precise wind data is available. The case studies are based on historical planned and flown flights.
Ali Alizadeh; Mevlut Uzun; Emre Koyuncu; Gokhan Inalhan. Optimal En-Route Trajectory Planning based on Wind Information. IFAC-PapersOnLine 2018, 51, 180 -185.
AMA StyleAli Alizadeh, Mevlut Uzun, Emre Koyuncu, Gokhan Inalhan. Optimal En-Route Trajectory Planning based on Wind Information. IFAC-PapersOnLine. 2018; 51 (9):180-185.
Chicago/Turabian StyleAli Alizadeh; Mevlut Uzun; Emre Koyuncu; Gokhan Inalhan. 2018. "Optimal En-Route Trajectory Planning based on Wind Information." IFAC-PapersOnLine 51, no. 9: 180-185.