Enhancing UAV manoeuvres and control using distributed sensor arrays

About the project

We are looking for a talented and motivated PhD student to develop flight-control methods to increase manoeuvrability of Uncrewed Aerial Vehicles (UAVs).

In the last decade UAV systems have experienced significant development and by 2050, UAV-based services are estimated to create markets worth over £600 billion. These services are expected to range from delivery of goods and medical supplies to inspection and maintenance of energy infrastructure.

To reach their full potential UAV systems need to safely operate in complex environments, where external perturbations (for example, wind gusts, obstacle avoidance, clutter environment) are difficult to sense and predict. Operating in such environments requires agile manoeuvring but conventional controllers limit the exploitable operational envelope of these aircraft. Your PhD will address this by integrating distributed sensing and nonlinear flight control techniques.

In this project you will assess the performance of different flight control strategies that use distributed sensing to achieve agile UAV manoeuvring. You’ll gain experience working with 3 novel technologies to enhance agile UAV manoeuvring: bio-inspired distributed sensing, machine-learning-based flight control and wind tunnel dynamic testing.

You will use an in-house bio-inspired distributed sensing system, which enables in-flight estimation of aerodynamic states and loads. You will apply machine learning to develop flight controllers that can fully exploit the information from the distributed sensing arrays.

You will be able to develop and simulate the performance of your algorithms with our high-performance computing facility, Iridis. Lastly, you'll use the R J Mitchell Wind Tunnel to conduct wind tunnel dynamic testing. This will enable you to model and characterise your design and to test and evaluate your flight controllers under real aerodynamic conditions.

As a PhD student at the University of Southampton you will join the Computational Engineering and Design Group (CEDG). You will work closely with Soton UAV, our world leading drone research lab, benefiting from training in the latest drone technologies.

Dr Sergio Araujo-Estrada BEng, MSc, PhD, MRAeS

• Autonomous vehicle dynamics (e.g., aircraft dynamics)
• Flight control systems (e.g., VTOL aircraft control, nonlinear control methods, adaptive AI flight controllers)
• Distributed sensing for aerospace systems (e.g., aerodynamic state estimation)

Entry requirements

A UK 2:1 honours degree, or its international equivalent .

Fees and funding

For UK students, tuition fees will be paid and you'll receive a stipend (living allowance) of £17,668 tax-free per year for up to 3.5 years.

How to apply

Apply now

You need to:

Applications should include:

• choose programme type (Research), 2023/24, Faculty of Engineering and Physical Sciences
• choose PhD in Engineering & Environment (Full time)
• add supervisor Doctor Sergio Araujo-Estrada in section 2
• your CV (resumé)
• 2 reference letters
• degree transcripts to date

Faculty of Engineering and Physical Sciences

If you have a general question, email our doctoral college (feps-pgr-apply@soton.ac.uk) .

Project leader

For an initial conversation, email Dr Sergio Araujo-Estrada, Computational Engineering and Design Research Group, Email: S.Araujo-Estrada@soton.ac.uk , Tel: +44 (0) 2380 59 2215.

Research areas