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Postgraduate research project

Learning-based Control and its applications to marine systems and renewable energy systems

Competition funded
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Type of degree
Doctor of Philosophy
Entry requirements
2:1 honours degree
(View full entry requirements)
Faculty graduate school
Faculty of Engineering and Physical Sciences
Closing date

About the project

The project aims to develop and investigate learning-based and optimisation-based control strategies (for example, model predictive control, AI, adaptive dynamic programming) 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. 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.

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

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