Control of Nonlinear Engineering Systems: Traditional and AI-Integrated Approaches

These projects are open to students worldwide, but have no funding attached. Therefore, the successful applicant will be expected to fund tuition fees at the relevant level (home or international) and any applicable additional research costs. Please consider this before applying.

The real world is inherently nonlinear, particularly in mechanical systems where nonlinearities arise from system geometry, material interactions, and nonlinear elements such as stiffness, damping, and friction. These complexities often lead to undesirable behaviours in engineering systems, such as instabilities, limit cycles, and chaotic dynamics. Advances in nonlinear dynamics, along with emerging tools such as AI and ML, offer exciting opportunities to address these challenges and develop efficient, adaptive control strategies for complex systems.

This project focuses on designing and implementing advanced control schemes for nonlinear systems to maintain desired behaviours and mitigate undesired dynamics. These schemes might leverage AI/ML to address challenges such as actuator, sensor, and communication delays, as well as output constraints, or they may employ traditional approaches tailored to specific system requirements. The integration of AI/ML, where suitable, can enable real-time optimisation, prediction, and decision-making, enhancing control performance under complex conditions. Alternatively, the research may explore non-AI-based methods that prioritise simplicity, cost-effectiveness, and reliability.

The project will encompass one or more case studies on diverse dynamical systems, potentially including renewable energy systems like floating wind turbines, self-balancing robots, or other systems selected at the project's onset.

A successful candidate will collaborate with the interdisciplinary Centre for Applied Dynamic Research (CADR) and the Artificial Intelligence, Robotics, and Mechatronic Systems Group (ARMS) at the School of Engineering, University of Aberdeen. They will have access to leading experts in the field, state-of-the-art laboratory facilities, and opportunities to explore both AI/ML-driven and traditional control strategies, depending on the project's direction and focus.

Candidates must have a strong academic background in Electrical / Mechanical / Mechatronics Engineering, Applied Physics / Mathematics can apply. Candidates with a solid 2-1 at honours level degree in these disciplines will also be considered if they can show adequate competence in the underlying concepts.

Familiarity with any two of the following subject areas is required:

i. Mathematical modelling of systems

ii. System kinematics and dynamics

iii. Nonlinear dynamics

Candidates must be competent with MATLAB and SIMULINK and / or similar mathematical software.

Application Procedure:

Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php.

You should apply for PhD in Engineering to ensure your application is passed to the correct team for processing.

Please clearly note the name of the lead supervisor and project title on the application form. If you do not include these details, it may not be considered for the studentship.

Your application must include: A personal statement, an up-to-date copy of your academic CV, and clear copies of your educational certificates and transcripts.

Please note: you do not need to provide a research proposal with this application.

Informal enquiries can be made by contacting Dr V Vaziri at vahid.vaziri@abdn.ac.uk.

If you require any additional assistance in submitting your application or have any queries about the application process, please don't hesitate to contact us at researchadmissions@abdn.ac.uk.

Funding Notes

This is a self-funding project open to students worldwide. Our typical start dates for this programme are February or October.

Fees for this programme can be found here Finance and Funding | Study Here | The University of Aberdeen