Dr Davood Asadi Hendoustani

• Qualifications:PhD in Aerospace Engineering - Flight Dynamics and Control
• Position:Flight Dynamics and Control
• Department:College of Arts, Technology and Environment
• Telephone:+441173283398
• Email:Davood.AsadiHendoustani@uwe.ac.uk
• Social media:

About me

Davood is engaged in aerospace engineering research at UWE Bristol, with a focus on flight dynamics and control systems. His expertise lies in flight dynamics, control, and aerial robotics, with a strong focus on autonomous flight, fault-tolerant control, and AI-driven aerial systems. At his academic posts in Turkey, he founded and led the Intelligent Flight Systems Lab. He has directed and contributed to several national and international funded projects on fault detection, autonomous landing, morphing aircraft, hybrid electric multirotor, and aerial swarm intelligence. His research outputs include publications in leading journals such as AIAA Journal, IEEE Transactions, control engineering practice, aerospace science and technology. Davood is the holder of UK global talent visa and recognized in the Top 2% of Scientists worldwide (Stanford University & Elsevier, 2023 and 2024). Davood also brings industry experience as CEO of INOVA TECHS, developing flight simulators for training. His work bridges academic research and industrial application.

Google Scholar: https://scholar.google.com/citations?user=PuZeNxwAAAAJ&hl=en

ResearchGate: https://www.researchgate.net/profile/Davood-Asadi-4?ev=hdr_xprf

LinkedIn: https://www.linkedin.com/in/davood-asadi-62521354/

Area of expertise

Davood’s Research Focus

• Flight Dynamics & Autonomous Systems
  Advanced modeling, stability analysis, and control of fixed-wing and multirotor aerial vehicles.
• Navigation, Flight Planning & Guidance
  Intelligent autonomous navigation, optimal trajectory generation, and real-time flight planning in complex environments.
• Fault Detection & Fault-Tolerant Control (FTC)
  Model-based, data-driven, and learning-based methods for detecting, isolating, and mitigating motor, actuator, and sensor faults in UAVs.
• Vision-Based & Sensor-Driven Autonomous Landing
  Precision landing in constrained and unstructured environments using optical flow, deep learning, and multi-sensor fusion.
• Cooperative UAV Swarms
  Decentralized control, communication-aware coordination, and cooperative search-and-rescue and environmental monitoring missions.
• Morphing Aircraft & Aeroelastic Control
  Bio-inspired morphing concepts, structural–aerodynamic coupling, and aeroelastic control for performance enhancement.
• Machine Learning & Deep Learning in Aerial Robotics
  Learning-based perception, decision-making, and anomaly detection for next-generation UAV autonomy.
• Path Planning & Emergency Flight Management
  Risk-aware routing, obstacle avoidance, fault-resilient maneuvers, and emergency landing strategies.
• Simulation, HIL Testing & Experimental Validation
  High-fidelity simulation and Hardware-in-the-Loop (HIL) experiments using Pixhawk, ROS, custom UAV platforms, and multi-axis testbeds.

Publications

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