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Topic

Data-Driven Real-Time Model Identification of UAS for Adaptive Control

Department of Mechanical Engineering

Date & location

• Monday, April 14, 2025

• 10:00 A.M.

• Virtual Defence

Reviewers

Supervisory Committee

• Dr. Afzal Suleman, Department of Mechanical Engineering, University of Victoria (Supervisor)

• Dr. Yang Shi, Department of Mechanical Engineering, UVic (Member)

• Dr. Phalguni Mukhopadhyaya, Department of Civil Engineering, UVic (Outside Member) 

External Examiner

• Dr. Kouamana Bousson, Department of Aerospace Sciences, University of Beira Interior 

Chair of Oral Examination

• Dr. Chris Darimont, Department of Geography, UVic

   

Abstract

This dissertation presents a comprehensive investigation into the development, modeling, and control of novel unmanned aerial vehicles (UAVs) within the Eusphyra project. Structured as a thesis-by-publication, the work delivers significant advancements in UAV design, flight dynamics modeling, autopilot tuning, and adaptive control, offering innovative methodologies to enhance performance and autonomy. The research begins with the design and airworthiness assessment of the Eusphyra UAV, detailing an iterative development process that culminates in the validation of an innovative tri-rotor VTOL configuration. A high-fidelity flight dynamics model is then developed using limited onboard sensor data and state-of-the-art system identification techniques to capture the complex aero-propulsive coupling inherent in the system. This model is rigorously validated against out-of-sample flight data, confirming its reliability and predictive capability. Building on these foundational insights, an automated offline autopilot tuning framework is introduced that leverages a simplified system identification process in conjunction with genetic algorithms. This approach minimizes human oversight and enables rapid returning in response to design modifications. Further extending the scope of the work, the dissertation explores real-time system identification by integrating unsupervised learning techniques to dynamically update UAV models during f light. This capability is advanced into the development of a Model Identification Adaptive Controller (MIAC), which combines Sparse Identification of Nonlinear Dynamics (SINDy) with Model Predictive Control (MPC) for adaptive, online control under varying flight conditions. Comprehensive hardware-in-the-loop simulations and flight tests confirm the feasibility and performance of MIAC, marking a significant step forward in UAV autonomy and adaptability, and laying the groundwork for future research in advanced adaptive control for complex aerial systems.