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DTPSCIDM: MASSIVELY PARALLELISABLE MODEL PREDICTIVE CONTROL FOR SAFE COLLABORATIVE ROBOTS AND AUTONOMOUS VEHICLES


School of Electronics, Electrical Engineering and Computer Science

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Dr Pantelis Sopasakis No more applications being accepted Competition Funded PhD Project (Students Worldwide)
Belfast United Kingdom Applied Mathematics Computational Mathematics Control Systems Dynamics Electrical Engineering Engineering Mathematics Manufacturing Engineering Probability Robotics Systems Engineering

About the Project

Supervisors: Dr Pantelis Sopasakis (EEECS) and Dr John McAllister (EEECS)

For further information, please contact Dr Pantelis Sopasakis at [Email Address Removed].

PROJECT DESCRIPTION

Under the hood of most control, machine learning and data analytics lie optimisation problems. Our ability to solve them in real time will be a determining factor for the development of next-generation applications including autonomous vehicles and collaborative robots.

To that end, it is imperative that we devise appropriate numerical algorithms to harness the unparalleled computation power of graphics processing units (GPUs).

This PhD project will focus on the development of massively parallelisable numerical optimisation algorithms to underpin optimisation-based controllers for autonomous vehicles, with special emphasis on autonomous heavy equipment.

The successful PhD student will devise an optimisation-based control and learning framework for collaborative robots (cobots) and autonomous vehicles (AV) building up on the novel risk-averse model predictive control framework developed by Dr Sopasakis.

The main challenges that will be addressed are the following:

• Cobots and autonomous vehicles are exposed to highly uncertain environments where safety is the first priority; existing control methodologies have turned out to be either too conservative or unsafe

Model predictive control is the golden standard in modern industrial automation; however, it often leads to optimisation problems that cannot be solved using existing methods, software and hardware.

The objectives of this project are:

• to develop a novel control-theoretic data-driven control framework that will furnish autonomous systems with increased reliability and safety and will allow the deployment of safe collaborative robotics; special emphasis will be given to large heavy equipment used in the construction industry,

• to develop new massively parallelisable numerical methods that can run on graphics processing units and will allow the solution of large-scale optimisation problems within the stringent runtime requirements of cobotics/AV applications (a few milliseconds),

• to demonstrate the effectiveness of the new control and optimisation methodology on collaborative robotic systems that will be developed by the student; this part of the project will be supported by EquipmentShare: a USA-based company that will offer access to their large-scale experimental facilities and autonomous heavy equipment that is equipped with multiple sensors and embedded computing resources.


ELECTRICAL & ELECTRONIC ENGINEERING OVERVIEW

The School of Electronics, Electrical Engineering and Computer Science (EEECS) aims to enhance the way we use technology in communication, data science, computing systems, cyber security, power electronics, intelligent control, and many related areas.

You'll be part of a dynamic doctoral research environment and will study alongside students from over 40 countries worldwide; we supervise students undertaking research in key areas of electronics and electrical engineering, including: power electronics,robotics, wireless communications, cybersecurity and sensor-based systems. As part of a lively community of over 100 full-time and part-time research students you’ll have the opportunity to develop your research potential in a vibrant research community that prioritises the cross-fertilisation of ideas and innovation in the advancement of knowledge.

Within the School we have a number of specialist research centres including a Global Research Institute, the Institute of Electronics, Communications and Information Technology (ECIT) specialising in Cyber Security, Wireless Innovation and Data Science and scalable computing.

Many PhD studentships attract scholarships and top-up supplements. PhD programmes provide our students with the opportunity to acquire an extensive training in research techniques.


Electrical & Electronic Engineering Highlights

Professional Accreditations

ECIT brings together, in one building, internationally recognised research groups specialising in key areas of advanced digital and communications technology.

Industry Links

CSIT brings together research specialists in complementary fields such as data security, network security systems, wireless-enabled security systems, intelligent surveillance systems; and serves as the national point of reference for knowledge transfer in these areas.

Electric Power and Energy Systems research is focused on problems related to distributed sources of energy and their integration into power networks. The cluster is a member of the IET Power Academy and is a major collaborator on all-island energy research.

SoCaM is dedicated to the design of advanced, integrated, high-speed wireless and couples activities in High Frequency Electronics, System-on-Chip, Signals and Systems and Digital Signal Processing, and for Gigabit/sec wireless.

World Class Facilities

The Institute of Electronics, Communications and Information Technology, with state-of-the-art technology, offers a bespoke research environment.

Internationally Renowned Experts

You will be working under the supervision of leading international academic experts.


Key facts

Research students are encouraged to play a full and active role in relation to the wide range of research activities undertaken within the School and there are many resources available including: Access to the Queen's University Postgraduate Researcher Development Programme | A wide range of personal development and specialist training courses offered through the Personal Development programme | Office accommodation with access to computing facilities and support to attend conferences for full-time PhD students

HOW TO APPLY

Apply here

For your application you will need to upload a single PDF file including:

1. Your CV

2. Academic transcripts

3. A short research proposal: Queen's University has released some guidelines on how to write a good proposal that could help you. This PhD project has a lot of different aspects that can involve control theory, parallelisable algorithms, numerical optimisation, robotics and more. In your research proposal you have the opportunity to elaborate on what you plan to do in your PhD.


Funding Notes

EquipmentShare is a USA-based company that will offer a top-up of up to £45,000 and will offer an internship for a period of 6 months where the PhD student will have access to their experimental facilities.
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