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Model Predictive Control for Autonomous Vehicles and Robots [Self-Funded Students Only]

   Cardiff School of Computer Science & Informatics

  , Dr Oktay Karakus  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Model predictive control (MPC) is a family of modern control methods that have demonstrated impressive performance in a variety of scenarios, including in robotics and autonomous vehicle control. Such methods rely on models that can predict a system's future behaviour in response to control inputs in order to find the optimal actions that drive the system to a desired state. MPC methods can handle highly non-linear systems, with complex interactions between inputs and outputs.

While MPC is now an established field, a number of questions remain open. This project aims to further enrich this family of methods by using state-of-the-art machine learning techniques to improve predictive modelling. In particular, one or more of the following topics will be investigated:

  1. Effective techniques for on-line learning of predictive models from sparse training data.
  2. Explicit modelling of uncertainties in the predictive models.
  3. Multi-modal and multi-objective control optimisation.
  4. Modelling interactions with potentially dynamic environment.

The candidate will be working within an expert team at the forefront of modern control theory and state-of-the-art machine learning. This project will thus equip the student with specialist knowledge in advanced computer science focusing on transferable skills in machine learning (especially deep learning), data mining, and high-performance numerical computing.

The prospective candidate will perform both theoretical research and carry out experimental work (in simulations and potentially with autonomous vehicle prototypes). Thus, an interest and practical experience in electronics and robotics will be a bonus. The candidate will also be encouraged to join, and ideally to lead, the emerging Cardiff Autonomous Racing team.

Keywords: machine learning, control theory, robotics

Contact for information on the project:

Academic criteria: A 2:1 Honours undergraduate degree or a master's degree, in computing or a related subject. Applicants with appropriate professional experience are also considered. Degree-level mathematics (or equivalent) is required for research in some project areas. 

Applicants for whom English is not their first language must demonstrate proficiency by obtaining an IELTS score of at least 6.5 overall, with a minimum of 6.0 in each skills component. 

How to apply:

Please contact the supervisors of the project prior to submitting your application to discuss and develop an individual research proposal that builds on the information provided in this advert. Once you have developed the proposal with support from the supervisors, please submit your application following the instructions provided below

This project is accepting applications all year round, for self-funded candidates via 

In order to be considered candidates must submit the following information: 

  • Supporting statement 
  • CV 
  • In the ‘Research Proposal’ section of the application enter the name of the project you are applying to and upload your Individual research proposal, as mentioned above in BOLD
  • Qualification certificates and Transcripts
  • Proof of Funding. For example, a letter of intent from your sponsor or confirmation of self-funded status (In the funding field of your application, insert Self-Funded)
  • References x 2 
  • Proof of English language (if applicable)

Interview - If the application meets the entrance requirements, you will be invited to an interview

If you have any additional questions or need more information, please contact  

Funding Notes

This project is offered for self-funded students only, or those with their own sponsorship or scholarship award.
Please note that a PhD Scholarship may also available for this PhD project. If you are interested in applying for a PhD Scholarship, please search FindAPhD for this specific project title, supervisor or School within its Scholarships category.

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