School of Electronics & Computer Science

Location:  Highfield Campus
Closing Date:  Friday 25 June 2021
Reference:  1366921FP

Supervisory Team:    Matthew Turner & Steven Gunn

REACTOR: Robust Embedded Adaptive Control Techniques for Online Reconfiguration

Project description

Next-generation control systems for aircraft and autonomous systems will inevitably feature elements of adaptation. They will be required to work in uncertain and dynamic environments, needing fast adaptation and reconfiguration. Examples are sophisticated UAVs (uninhabited aerial vehicles) and other autonomous vehicles required to undertake multiple mission-types, collaborative activities with other agents, and possibly to execute “limp-home” recovery modes in the event of damage.

Conventional adaptive control algorithms have limited potential to offer this level of adaptability: they consist of fixed structure control laws supplemented with rudimentary algorithms which update these structures based on the current environment. These update algorithms are based mainly on stability considerations required to guarantee some level of safe operation, but are unsophisticated, with limited, inflexible learning potential. Their key advantage is the ease in which they can be implemented on modest hardware.

Techniques from artificial intelligence (AI) offer, potentially, an appealing alternative to the existing rudimentary algorithms for update of the adaptive control laws. These techniques offer more flexibility, higher performance and also may improve the other well-known limitation in adaptive control: robustness. Their key deficiencies are their difficulty in implementation on embedded devices and the difficulties in guaranteeing stability (the minimum requirement for control systems). 

This research will develop adaptive-AI based control laws for edge computing, thereby enabling the implementation of such algorithms on mobile, network-deprived autonomous systems. The focus of the research will be on discovering appropriate holistic approaches for combing adaptive controllers with AI update laws.

This project is funded through the UKRI MINDS Centre for Doctoral Training (www.mindscdt.ai). This is one of 16 PhD training centres in the UK with a unique focus on advancing AI techniques in the context of real-world engineered systems with a remit that spans novel hardware for AI, AI and machine learning, pervasive systems and IoT, and human-AI collaboration. We provide enhanced cross-disciplinary training in electronics and AI, entrepreneurship, responsible research and innovation, communication strategies, outreach and impact development as part of an integrated 4-year iPhD programme. 

The MINDS CDT is based in a dedicated laboratory on Highfield Campus at the University of Southampton. The lab provides a supportive environment for individual research, ideas sharing and collaboration, and the wider campus provides access to substantial high-performance computing (including dedicated GPU servers), maker and cleanroom facilities. You will take part in our annual, student-designed innovation camps, be able to work with industry and government partners through our internship scheme and be able to take part in exchanges with international university partners.

Entry Requirements

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

Closing date: 25 June 2021 for entry in October 2021.

Funding: full tuition for UK Students an enhanced stipend £18,285, tax-free per annum for 4 years.

How To Apply

Applications should be made online. Select programme type (Research), 2021/22, Faculty of Physical Sciences and Engineering, next page select “iPhD Machine Intelligence for Nano-electronic Devices and Systems. (Full time)”. In Section 2 of the application form you should insert the project title and name of the supervisor. 

Applications should include: 

• Research Proposal
• Curriculum Vitae
• Two reference letters
• Degree Transcripts to date

Apply online: https://www.southampton.ac.uk/courses/how-to-apply/postgraduate-applications.page

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