Contact Matthew Osborne
Areas of expertise
- Aeronautical Systems
- Autonomous Systems
- Vehicle Aerodynamics
- Vehicle Health Management
Background
Matthew completed his BEng in Aerospace Engineering from Southampton University and then spent time at QinetiQ as a systems engineer working on autonomous landing technology. After spending time in the 3D graphics world for the Bionic Group at Pinewood Studios. He completed his MSc in Aerospace Dynamics at Cranfield in 2012 and went on to work as an Aerodynamicist at SAIC Motors and then Jaguar Land Rover as a lead test and development engineeer. After a preparatory year at Heriot-Watt University as part of a CDT in Embedded Intelligence Matthew returned to Cranfield University to pursue a PhD in Aerospace studying Safe Online Learning for Flight Control Systems for Fixed Wing Aircraft.
Current activities
Matthew is currently researching Safe Online Learning for Nonlinear Control Systems. Research interests include function approximation techniques, model-based and hybrid model-free techniques, sparse Gaussian process techniques, convex optimisation and contraction based safety certification. The research is focused on fixed wing architectures and practical online learning for safe exploration of the flight envelope, improved safety, increased performance and control and reduced costs for development.
Publications
Conference Papers
- Osborne M, Shin H-S & Tsourdos A (2021) A review of safe online learning for nonlinear control systems. In: 2021 International Conference on Unmanned Aircraft Systems (ICUAS), Athens, 15-18 June 2021.
- Osborne M, Lantair J, Shafiq Z, Zhao X, Robu V, Flynn D & Perry J (2020) UAS operators safety and reliability survey: emerging technologies towards the certification of autonomous UAS. In: 2019 4th International Conference on System Reliability and Safety (ICSRS), Rome, 20-22 November 2019.
- Zhao X, Osborne M, Lantair J, Robu V, Flynn D, Huang X, Fisher M, Papacchini F & Ferrando A (2019) Towards integrating formal verification of autonomous robots with battery prognostics and health management. In: SEFM: International Conference on Software Engineering and Formal Methods, Amsterdam, 14-18 September 2020.