Data-driven real-time model identification of UAS for adaptive control
| dc.contributor.author | Bazzocchi, Sean | |
| dc.contributor.supervisor | Suleman, Afzal | |
| dc.date.accessioned | 2025-04-25T22:46:11Z | |
| dc.date.available | 2025-04-25T22:46:11Z | |
| dc.date.issued | 2025 | |
| dc.degree.department | Department of Mechanical Engineering | |
| dc.degree.level | Doctor of Philosophy PhD | |
| dc.description.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 retuning 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 flight. 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. | |
| dc.description.scholarlevel | Graduate | |
| dc.identifier.bibliographicCitation | Bazzocchi, S., Suleman, A. (2023). In-Flight Nonlinear System Identification for UAS Adaptive Control. In: Karakoc, T.H., Yilmaz, N., Dalkiran, A., Ercan, A.H. (eds) New Achievements in Unmanned Systems. ISUDEF 2021. Sustainable Aviation. Springer, Cham. [https://doi.org/10.1007/978-3-031-29933-9_19] | |
| dc.identifier.bibliographicCitation | Arco, A., Lobo Do Vale, J., Bazzocchi, S., Suleman, A. (2023). Investigation on the Airworthiness of a Novel Tri-Rotor Configuration for a Fixed Wing VTOL Aircraft. International Journal of Aviation Science and Technology, 04(02), 53-62. [https://doi.org/10.23890/IJAST.vm04is02.0201] | |
| dc.identifier.bibliographicCitation | Figueira, J. C., Bazzocchi, S., Warwick, S., Suleman, A. (2024). Nonlinear Aero-Propulsive Modeling for Fixed-Wing eVTOL UAV from Flight Test Data. In: Journal of Aircraft, pp. 1-13. [https://doi.org/10.2514/1.C037964] | |
| dc.identifier.bibliographicCitation | Bazzocchi, S., Suleman, A. (2024). Non-linear System Identification for UAS Adaptive Control. In: Karakoc, T.H., Özbek, E. (eds) Unmanned Aerial Vehicle Design and Technology. Sustainable Aviation. Springer, Cham. [https://doi.org/10.1007/978-3-031-45321-2_10] | |
| dc.identifier.bibliographicCitation | Arco, A., do Vale, J.L., Bazzocchi, S., Suleman, A. (2024). Conceptual Design, Development, Test and System Identification of a Novel Tri-Rotor Configuration for a VTOL Fixed Wing Aircraft. In: Karakoc, T.H., et al. Novel Techniques in Maintenance, Repair, and Overhaul. ISATECH 2022. Sustainable Aviation. Springer, Cham. [https://doi.org/10.1007/978-3-031-42041-2_28] | |
| dc.identifier.bibliographicCitation | Bazzocchi, S., Warwick, S., Suleman, A. (2024). Automatic autopilot tuning framework using genetic algorithms and system identification. In: Aerospace Science and Technology, Volume 157, 2025, 109779, ISSN 1270-9638. [https://doi.org/10.1016/j.ast.2024.109779] | |
| dc.identifier.uri | https://hdl.handle.net/1828/22018 | |
| dc.language | English | eng |
| dc.language.iso | en | |
| dc.rights | Available to the World Wide Web | |
| dc.subject | system identification | |
| dc.subject | uav | |
| dc.subject | model identification adaptive controller | |
| dc.subject | autopilot tuning | |
| dc.subject | online system idetification | |
| dc.subject | vtol uas | |
| dc.title | Data-driven real-time model identification of UAS for adaptive control | |
| dc.type | Thesis |