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PhD Studentship: Learning-based Control and its Applications to Marine Systems and Renewable Energy Systems

University of Southampton

Qualification Type: PhD
Location: Southampton
Funding for: UK Students
Funding amount: £17,668 tax-free per annum
Hours: Full Time
Placed On: 5th December 2022
Closes: 1st July 2023
 

Supervisory Team: Dr. Yao Zhang, Prof. Stephen Turnock 

Project description

Applications are invited for a 3.5 years PhD studentship in School of Engineering, at University of Southampton, in the field of Dynamics and Control (Ship science Research Group). The project aims to develop and investigate learning-based and optimization-based control strategies (e.g. model predictive control, AI, adaptive dynamic programming etc.) and their applications to the marine operations. 

Currently, many marine operations, such as launch and recovery (L&R) from a mother ship of small craft, manned and unmanned air vehicles, and submersibles, can only be attempted in sufficiently clam sea states. Typically, the wave-critical high-risk elements of the overall recovery task, i.e., the connection and subsequent hoist of the small craft to the parent vessel, only last a few tens of seconds. (Increasing the recovery time increases the operational risk.) In the case where a human initiates recovery, once the two crafts are physically connected, the operator is committed to commence the hoisting process. To automate this process, in this project, the rescue boat is launched and connected to the mother ship by a cable, and the hoisting process is achieved by a crane fixed on the mother ship. The movements of both the mother ship and the small boat are subject to wave forces. Due to unmeasurable wave forces and high nonlinearity in the dynamical model, learning-based control schemes will be investigated to achieve a fast and safe recovery subject to partially known models and also reduce the dependency and limitation of the sea states. 

Applicants shall have demonstrated experience in optimization, estimation theory and Kalman filters, and classical and modern control techniques, with particular focus on learning-based control methods (e.g. model predictive control, AI, adaptive dynamic programming etc.). Experience in computer programming (e.g. Python, C++) and relevant engineering software (e.g., MATLAB/Simulink) is an essential asset. Applicants should demonstrate proficiency in technical writing, and a track record with publications in well-recognized journals and conferences is preferred.  

The successful PhD candidate will be based at University of Southampton with the possibility of visiting and collaborating with Tsinghua University and Harbin Engineering University. Within this project, the candidate will build a strong background in dynamics and control as well as optimization. Upon successful completion of the PhD, the candidate will be able to carry out independent research and development activities in research centres and companies. 

Entry Requirements

A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent). 

Closing Date: Applications should be received no later than 01 July 2023 for standard admissions, but later applications may be considered depending on the funds remaining in place. 

Funding: For UK students, Tuition Fees and a stipend of £17,668 tax-free per annum for up to 3.5 years. 

How To Apply

Apply online: Search for a Postgraduate Programme of Study (soton.ac.uk). Select programme type (Research), 2023/24, Faculty of Physical Sciences and Engineering, nxt page select “PhD Engineering & Environment (Full time)”. In Section 2 of the application form you should insert the name of the supervisor Yao Zhang 

Applications should include:

Research Proposal

Curriculum Vitae

Two reference letters

Degree Transcripts/Certificates to date 

For further information please contact: feps-pgr-apply@soton.ac.uk

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