Control of aerial vehicles in extreme conditions

The development of low-cost aerial vehicles has rendered their use popular in a number of applications such as the inspection of critical infrastructure (e.g., bridges), surveying of underground mines, search-and-rescue operations and precision agriculture. It is often necessary to deploy several aerial vehicles that need to operate in obstructed environments and in a wide range of weather conditions. The main aim of this project will be to leverage recent advances in numerical optimisation to develop novel control and estimation methodologies that will allow the operation of autonomous aerial vehicles in extreme weather conditions and in obstructed geometries – one example is the inspection of offshore floating wind turbines; the aerial vehicle will have to fly at a high altitude close to a moving blade and will be affected both by the wind and by the wake effect of the blades.

Project Description:

In this project, the successful candidate will develop stochastic model predictive control (SMPC) methodologies for aerial vehicles (e.g., quadcopters) and state/parameter estimation methodologies based on moving horizon estimation (MHE) that will be able to run on simple embedded devices at rates as high as 100Hz. In this project the successful candidate will

1. Design aerial vehicles that will be able to identify obstacles in their vicinity, predict their trajectories and avoid collisions; to that end, we will employ model predictive control and moving horizon estimation methodologies.
2. Develop a stochastic control and estimation framework and will enable the vehicle to respond swiftly and safely to gusts of wind or unexpected changes of the obstacle trajectories
3. Develop a control and estimation framework of swarms of aerial vehicles that will allow them to perform collaborative tasks under extreme weather conditions and moving obstacles
4. Devise ad-hoc numerical optimisation algorithms that will allow the fast execution of MPC and MHE problems
5. Conduct lab experiments with aerial vehicles to demonstrate the effectiveness of the above control and estimation methodologies

Project Key Words: Model predictive control; aerial vehicles; micro aerial vehicles (MAVs); UAVs.

Start Date: 01/10/22

Application Closing date: 28/02/22

For further information about eligibility criteria please refer to the DfE Postgraduate Studentship Terms and Conditions 2021-22 at https://go.qub.ac.uk/dfeterms

Academic Requirements:

A minimum 2.1 honours degree or equivalent in Computer Science or Electrical and Electronic Engineering or relevant degree is required.

Funding Notes:

This three year studentship, for full-time PhD study, is potentially funded by the Department for the Economy (DfE) and commences on 1 October 2022. For UK domiciled students the value of an award includes the cost of approved tuition fees as well as maintenance support (Fees £4,500 pa and Stipend rate £15,609 pa - 2022-23 rates to be confirmed). To be considered eligible for a full DfE studentship award you must have been ordinarily resident in the United Kingdom for the full three year period before the first day of the first academic year of the course.

For candidates who do not meet the above residency requirements, a small number of international studentships may be available from the School. These are expected to be highly competitive, and a selection process will determine the strongest candidates across a range of School projects, who may then be offered funding for their chosen project.