Yazar "Khaneghaei, Mohammad" seçeneğine göre listele

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    A unified and experimentally validated design framework for long-endurance solar UAVS using model-based multi-objective multidisciplinary optimization
    (Springer, 2025) Khaneghaei, Mohammad; Asadi, Davood; Ebrahimi, Benyamin; Hazeri, Majid; Farsadi, Touraj; Nabavi Chashmi, Yaser; Durhasan, Tahir
    Designing long-endurance, solar-powered unmanned aerial vehicles (UAVs) requires careful coordination across aerodynamic, structural, and energy subsystems, particularly when targeting flexible, high-aspect-ratio configurations. This paper presents a mission-driven design and optimization framework for solar-powered long-endurance UAVs, tailored to post-disaster urban surveillance scenarios. A modular, multidisciplinary approach is adopted to account for the coupled effects of structural deformation and solar energy availability, both of which critically affect flight endurance. A key feature of the framework is the simultaneous integration of aeroelastic constraints and a time-dependent solar power and battery model, capturing realistic energy generation and storage behavior over diurnal cycles. This energy model is experimentally validated using a custom-built testbed and incorporated directly into the design loop. The framework is implemented using a Multidisciplinary Design Optimization (MDO) architecture that employs a coupling strategy to effectively manage interdependencies among subsystems. A comprehensive sensitivity analysis using Latin Hypercube Sampling highlights key performance-driving parameters. The final UAV design is fabricated and flight-tested, demonstrating the satisfaction of mission-level requirements derived from a simulated post-earthquake damage assessment in Adana, T & uuml;rkiye. Battery state-of-charge, trajectory, and attitude data collected during flight tests demonstrate that the UAV operates in accordance with design predictions, despite environmental variability. The study highlights how the integration of validated subsystem models within an established optimization process can lead to reliable, application-specific solar UAV designs suitable for real-world deployment.
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    An experimental vision-based integrated guidance and control strategy for autonomous landing of a faulty UAV
    (SAGE Publications Ltd, 2025) Khaneghaei, Mohammad; Asadi, Davood; Zahmatkesh, Mohsen; Tutsoy, Onder
    Uncrewed Aerial Vehicles (UAVs) have emerged as a transformative asset in surveillance, mapping, and delivery tasks since they have sophisticated autonomous capabilities. This paper develops a practical vision-based optimal flight planning strategy for autonomous safe landing and control of a multirotor UAV using a low-cost monocular camera in the presence of a motor fault. An optimal integrated guidance and control strategy is developed by utilizing an innovative discrete system model and a state observer from the triggering point to the identified landing position. Additionally, compatible image processing techniques and UAV kinematics are integrated to detect the suitable landing site and translate its location into desired attitude inputs to the controller. This approach empowers the UAV to autonomously land in no-GPS environments, relying solely on camera data. Initially, vision-based sensors, image processing techniques, and the developed guidance and control algorithms undergo initial evaluation in Software in the Loop (SIL) simulations using the Robot Operating System (ROS) and Gazebo simulation environments. The efficacy of the proposed framework is then assessed through experimental flight tests across various landing scenarios, accounting for local wind conditions and motor faults.
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    Experimental motor fault detection and identification of a quadrotor UAV using a hybrid deep learning approach
    (Springer Nature, 2025) Khaneghaei, Mohammad; Asadi, Davood; Mowla, Md. Najmul; Disken, Gokay
    This study presents a novel experimental hybrid sequential deep learning (DL) approach for real-time motor fault detection and magnitude estimation in quadrotor UAVs, addressing critical gaps in current fault-tolerant control systems. The proposed framework integrates long short-term memory (LSTM) networks with 1D convolutional neural networks (1D-CNN) to enhance fault classification and estimation accuracy. The dual capability distinguishes the proposed model from existing methods, which often focus solely on fault detection without addressing magnitude estimation. A novel dataset, generated through Hardware-in-the-Loop (HIL) experiments, incorporates 25,000 unique fault scenarios under diverse configurations and flight conditions. This dataset strengthens the model's robustness and applicability to real-world scenarios. The developed architecture demonstrated superior performance, achieving 99.2% fault detection accuracy, surpassing existing methods in robustness and efficiency. By embedding the model into a dynamic HIL testbed, the study validates the framework's capability to detect faults, estimate magnitudes, and restore stability in quadrotors under challenging conditions. Experimental results highlight the system's effectiveness in reducing motor anomalies, ensuring improved operational safety and reliability. The approach is adaptable to broader UAV systems, offering significant advancements in autonomous fault-tolerant control.
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    Software in the Loop (SIL) Simulation for an Autonomous Multirotor Flight Planning and Landing with ROS and Gazebo
    (Institute of Electrical and Electronics Engineers Inc., 2023) Khaneghaei, Mohammad; Asadi, Davood; Tutsoy, Önder
    Unmanned Aerial Vehicles (UAVs) have emerged as a transformative asset in surveillance, mapping, and delivery tasks since they have sophisticated autonomous capabilities. This paper focuses on enhancing the UAV autonomy by addressing the autonomous detection of suitable landing sites and safe landing flight planning, which are both critical in emergency scenarios. To mitigate the possible risks during the flight tests, vision-based sensors and image processing techniques, along with the developed guidance and control algorithms are evaluated in Software in the Loop (SIL) simulation using the Robot Operating System (ROS) and Gazebo simulation environments. The key novelty of this paper is proposing a vision-based hybrid LQR-integrator flight planning strategy for safe landing and optimal control of the UAVs. Therefore, this paper incorporates the image processing techniques for the landing site detection and UAV kinematics for safe and autonomous landing on the selected site. Further contributions of this paper are the selection of a compatible image processing technique, translation of the selected site position data into a control reference frame, development of a novel controller strategy, and the utilization of an observer for destination verification. Through the comprehensive simulations using Gazebo and ROS, the paper evaluates the effectiveness of the proposed framework, paving the way for further real-world testing and implementation. © 2023 IEEE.