Machine Learning Approaches to Enhancing UAVs Optimal Control in Northern Ireland Climate for Industry 4.0

Summary

The Internet of Things (IoT), Big Data and drones, are all examples of implementable advanced technologies intended to accomplish Industry 4.0. Aerial robots, in particular Unmanned Aerial Vehicles (UAVs) and drones, have seen significant advancements in recent years in terms of their design, operation, flying capabilities, and navigational control. UAVs are applicable to a wide range of services, including photography, route planning, search and rescue, inspection of power lines and other civil structures.  However, UAVs suffer in Northern Irish winters due to heavy winds, preventing vital tasks such as search and rescue or civil engineering observations from being performed.

The purpose of this study is to improve the stability of drone technology for Industry 4.0 applications. For example, it is expected that this project will improve the stability of UAVs during firefighting and rescue scenarios. This PhD project will involve an enhanced stability study of UAVs under wind disturbances by using metaheuristic algorithms based on machine learning to select optimal controller gains. This will be optimized using optimal controllers such as Particle Swarm Optimization and Genetic Algorithms. The next phase of the project will implement machine learning algorithms to improve the stability of the UAVs.

Analysis will be carried out with machine learning based hybrid methods such as Full State Feedback, Full State Compensator and Linear Quadratic Gaussian controllers. The conditions required to employ Reinforcement Learning as an alternative method for UAV stabilization will also be explored in detail. Computational complexity and real-time deployment are additional critical features that will be explored.

The ideal candidate will have a Masters degree in computer science, mechatronics, electronic, electrical engineering or related discipline. Excellent mathematical and analytical skills, experience in programming, good knowledge of machine learning and experience of either Matlab or Python is essential.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

  • First Class Honours (1st) Degree
  • Masters at 70%
  • Experience using research methods or other approaches relevant to the subject domain
  • Work experience relevant to the proposed project
  • Publications - peer-reviewed

Funding and eligibility

The University offers the following levels of support:

Vice Chancellors Research Studentship (VCRS)

Full award (full-time PhD fees + DfE level of maintenance grant + RTSG for 3 years).

This scholarship will cover full-time PhD tuition fees and provide the recipient with £18,000 (tbc) maintenance grant per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Vice-Chancellor’s Research Bursary (VCRB)

Part award (full-time PhD fees + 50% DfE level of maintenance grant + RTSG for 3 years).

This scholarship will cover full-time PhD tuition fees and provide the recipient with £8,000 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Vice-Chancellor’s Research Fees Bursary (VCRFB)

Fees only award (PhD fees + RTSG for 3 years).

This scholarship will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £18,000 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

  • Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
  • Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
  • Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
  • Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living

Recommended reading

Gibson, J.; Hadi, M.U. Modeling and Optimal Control for Rotary Unmanned Aerial Vehicles in Northern Ireland Climate. Appl. Sci. 2022, 12, 7677.

Azar, A.T., Koubaa, A., Ali Mohamed, N., Ibrahim, H.A., Ibrahim, Z.F., Kazim, M., Ammar, A., Benjdira, B., Khamis, A.M., Hameed, I.A. and Casalino, G., 2021. Drone deep reinforcement learning: A review. Electronics, 10(9), p.999.

Huang, C.; Zhang, H. Comparison of Disturbance Rejection Performance between Three Types of UAV Linear Controllers. In Proceedings of the 7th International Conference on Information Science and Control Engineering (ICISCE), Changsha, China, 18–20 December 2020.

Y. Ouyang, X. Wang, R. Hu and H. Xu, "APER-DDQN: UAV Precise Airdrop Method Based on Deep Reinforcement Learning," in IEEE Access, vol. 10, pp. 50878-50891, 2022, doi: 10.1109/ACCESS.2022.3174105.

X. Fan, M. Liu, Y. Chen, S. Sun and Z. Li, "RIS-Assisted UAV for Fresh Data Collection in 3D Urban Environments: A Deep Reinforcement Learning Approach," in IEEE Transactions on Vehicular Technology, 2022, doi: 10.1109/TVT.2022.3203008.

The Doctoral College at Ulster University

Key dates

Submission deadline
Monday 27 February 2023
04:00PM

Interview Date
week commencing 17 April 2023

Preferred student start date
18 Sept 2023

Applying

Apply Online  

Contact supervisor

Professor Huiru (Jane) Zheng

Other supervisors

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