Available PhD Opportunities

Fractional Calculus is an exciting area with the potential to make key inroads into the wider area of system modelling and control. It lies at the cusp between linear and nonlinear system theory [1]. As a result, several recent applications of Fractional Calculus have been reported to improve the modelling and control of a wide range of systems. Fractional Calculus can be potentially applied to simplify the modelling of nonlinear phenomena, such as hysteresis and creep. Additionally, the almost infinite freedom fractional-order controllers provide, hitherto unexploited, can provide significant performance benefits to a wide range of systems. This thesis will focus on expanding the boundaries of application for Fractional Calculus. Our recent work has identified several research gaps as well as avenues to push knowledge boundaries [2, 3]. After an initial review of the state-of-the-art and aligning the research focus on individual student/s’s interests, this research can focus on system modelling (mechanical / electrical engineering, applied physics, bioengineering etc) or system control (new control theory / focussed control designs for applications such as precision positioning systems, drill-strings, robots etc), development, implementation, optimization, and validation.

Successful candidates will join the interdisciplinary Artificial Intelligence, Robotics and Mechatronic Systems Group (ARMS) at the School of Engineering, University of Aberdeen and will have access to area experts as well as a well-furnished laboratory for all their experimental studies, should they choose to explore that direction. They will also be involved (as opportunities arise) in short-term live industry projects as paid research assistants, allowing them to broaden their industry-relevant skillset. A limited number of paid teaching assistantship positions can also be availed (subject to budget restrictions and course requirements). Students will have the opportunity to collaborate with one or more of the authors of [1].

Prerequisites

Selection will be made on the basis of academic merit. The successful candidate should have, or expect to obtain, a UK Honours degree at 2.1 or above (or equivalent) in Electrical / Mechanical / Mechatronics Engineering, Applied Physics / Mathematics along with evidence of adequate competence in the underlying concepts.

• Mathematical Modelling of Systems 

• Systems kinematics and dynamics 

• Linear Algebra and Matrix Theory

• Linear Control Systems

• Nonlinear Control Systems

• Nonlinear dynamics 

• Differential Calculus

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

Application Procedure:

APPLICATION PROCEDURE:

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

• Apply for Degree of Doctor of Philosophy in Engineering

• State name of the lead supervisor as the Name of Proposed Supervisor

• State ‘Self-funded’ as Intended Source of Funding

• State the exact project title on the application form

When applying please ensure all required documents are attached:

• All degree certificates and transcripts (Undergraduate AND Postgraduate MSc-officially translated into English where necessary)

• Detailed CV, Personal Statement/Motivation Letter and Intended source of funding

Informal inquiries can be made to Dr S.Aphale  with a copy of your curriculum vitae and cover letter.  All general inquiries should be directed to the Postgraduate Research School.

References:

[1] C. A. Monje, YQ Chen, B. M. Vinagre, D. Xue and V. Feliu, Fractional-order systems and controls: fundamentals and applications, Springer, 2010.

[2] L. Li, Z. Chen, S. S. Aphale and LM Zhu, Fractional Repetitive Control of Nanopositioning Stages for High-Speed Scanning Using Low-Pass FIR Variable Fractional Delay Filter, IEEE/ASME Transactions on Mechatronics, vol. 25, no. 2, pp. 547 – 557, 2020

[3] A. San-Millan, V. Feliu-Batlle and S. S. Aphale, Fractional order implementation of Integral Resonant Control – A nanopositioning application, ISA Transactions, vol. 82, pp. 223-231, 2018 

Precision manufacturing, high-fidelity measurements and supersensitive equipment – all require an ultra-quite base / floor [1, 2]. The current Active Vibration Isolation technology as well as popularly employed control schemes fall short of the desired performance metrics. This research will first start with a detailed study of the state-of-the-art to clearly understand what the current performance boundaries are and why. Initial studies have demonstrated that dual-stage complimentary actuator platforms might be able to deliver a step-change in AVI performance.

Control of these platforms to fully exploit their performance capabilities is a multivariable control and optimization problem that is to date, unsolved. This research aims at first developing a digital-twin model of the dual-stage AVI Platform and then designing optimized MIMO control algorithms that allow for exploitation of the extreme limits of operation to deliver the required ultra-quiet performance. The control schemes developed within this research have the potential to be applied to other MIMO systems that collaborate to deliver a unified goal; for example multi-legged robots.

Successful candidates will join the interdisciplinary Artificial Intelligence, Robotics and Mechatronic Systems Group (ARMS) at the School of Engineering, University of Aberdeen and will have access to area experts as well as a well-furnished laboratory for all their experimental studies, should they choose to explore that direction. They will also be involved (as opportunities arise) in short-term live industry projects as paid research assistants, allowing them to broaden their industry-relevant skillset. A limited number of paid teaching assistantship positions can also be availed (subject to budget restrictions and course requirements). Students will have the opportunity to collaborate with the Structural Vibrations Group at Exeter and Alcala Henares.

Prerequisites:

Selection will be made on the basis of academic merit. The successful candidate should have, or expect to obtain, a UK Honours degree at 2.1 or above (or equivalent) in Electrical / Mechanical / Mechatronics / Structural Engineering, Applied Physics / Mathematics along with evidence of adequate competence in the underlying concepts.

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

i.                    Mathematical modelling of systems

ii.                  Dynamics of structures

iii.                Linear Algebra and Matrix Theory

iv.                Control Systems

v.                  Mechanical / Structural Vibrations

vi.                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

• Apply for Degree of Doctor of Philosophy in Engineering

• State name of the lead supervisor as the Name of Proposed Supervisor

• State ‘Self-funded’ as Intended Source of Funding

• State the exact project title on the application form

When applying please ensure all required documents are attached:

• All degree certificates and transcripts (Undergraduate AND Postgraduate MSc-officially translated into English where necessary)

• Detailed CV, Personal Statement/Motivation Letter and Intended source of funding

Informal inquiries can be made to Dr S Aphale with a copy of your curriculum vitae and cover letter. All general inquiries should be directed to the Postgraduate Research School.

References:

[1] B. Bakker and J. van Seggelen, “The revolutionary Hummingbird technology”. Mikroniek 50 (3): 14–20, 2010.

[2] A. Preumont, M. Horodinca, I. Romanescu, B. de Marneffe, M. Avraam, A. Deraemaeker, F. Bossens, and A.A. Hanieh, “A six-axis single-stage active vibration isolator based on Stewart platform”. J. Sound Vibr. 300: 644–661, 2007.

The real world is inherently nonlinear. Particularly in mechanical systems, these nonlinearities arise from one or more of the following reasons: geometry of the system, materials applied interactions between parts of the system and nonlinear elements such as nonlinear stiffness damping and friction [1]. Moreover, such nonlinearities frequently cause undesirable behaviour in engineering structures, for example, instabilities, limit cycles, the coexistence of desired and undesired attractors or even chaos. Nevertheless, recent advances in nonlinear dynamics have developed a great potential, enabling a deeper understanding and analysis of complex systems.

Well-performing control schemes are usually employed to ensure the system maintains desired behaviour and steers away undesirable dynamics. These schemes are required synergistic interconnection of actuators and sensors. Actuators, such as electrical motors, hydraulic or pneumatic valves, do not typically have instantaneous responses, and their dynamics usually exhibit (i) delay and (ii) maximum/minimum value (constrained control effort) [2-3]. It was shown in [4] that the effect of simultaneous existence of both limitations could not be ignored as they would make the control unsuccessful, inefficient, and occasionally drive the controlled system to instability. Despite several linear, nonlinear and chaos control methods introduced theoretically in the last few decades, significant work is needed to develop control schemes that accommodate delays and/or actuator constraints. Therefore this project aims at advancing control schemes for time-delayed nonlinear systems with actuator constraints. As a result, the key objectives of this project are:

•        Nonlinear dynamics analysis and parametric study of selected systems in open-loop.

•        Development of new, high-performance control scheme[s] to minimise / eliminate performance limitations due to delay and control effort constraints.

•        Numerical investigation and experimental validation on the efficiency of the designed control scheme[s].

Successful candidates will join the interdisciplinary Centre for Applied Dynamic Research (CADR) & interdisciplinary Artificial Intelligence, Robotics and Mechatronic Systems Group (ARMS) at the School of Engineering, University of Aberdeen and will have access to area experts as well as a well-furnished laboratory for all their experimental studies, should they choose to explore that direction.

Prerequisites:

Selection will be made on academic merit. The successful candidate should have (or expect to achieve) a minimum of a UK Honours degree at 2.1 or above (or equivalent) in Electrical / Mechanical / Mechatronics Engineering, Applied Physics / Mathematics can apply. Candidates with a solid 2-1 degree in these disciplines will also be considered if they are able to 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.                Linear Algebra and Matrix Theory

iv.                Linear Control Systems

v.                  Nonlinear Control Systems

vi.                Nonlinear dynamics

vii.               Differential Calculus

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

• Apply for Degree of Doctor of Philosophy in Engineering

• State name of the lead supervisor as the Name of Proposed Supervisor

• State ‘Self-funded’ as Intended Source of Funding

• State the exact project title on the application form

When applying please ensure all required documents are attached:

• All degree certificates and transcripts (Undergraduate AND Postgraduate MSc-officially translated into English where necessary)

• Detailed CV, Personal Statement/Motivation Letter and Intended source of funding

Informal inquiries can be made to Dr V Vaziri with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Postgraduate Research School.

References:

[1] Slotine, J.J.E. and Li, W., 1991. Applied nonlinear control (Vol. 199, No. 1). Englewood Cliffs, NJ: Prentice hall.

[2] Liu, Y., Wiercigroch, M., Ing, J. and Pavlovskaia, E., 2013. Intermittent control of coexisting attractors. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371(1993), p.20120428.

[3] Vaziri, V., Kapitaniak, M. and Wiercigroch, M., 2018. Suppression of drill-string stick–slip vibration by sliding mode control: Numerical and experimental studies. European Journal of Applied Mathematics, 29(5), pp.805-825.

[4] Vaziri, V., Oladunjoye, I., Kapitaniak, M., Aphale, S.S. & Wiercigroch, M., A Parametric Analysis of a Sliding-Mode Controller Designed to Alleviate Drill-String Stick-Slip Vibrations, Meccanica, Accepted, March 2019.

At Aberdeen, we have several lines of research around the manufacture of green chemicals. For example, we are building plasma catalysis reactors to reform biomethane and/or carbon dioxide into more valuable chemicals as part of a Leverhulme trust sponsored doctoral training centre. We also continue to work on low-carbon cements produced via sulfur combustion sponsored by the GORD institute. Finally, we’re developing “electronic noses” which can monitor fermentation processes (and plasma reactors) using combinations of cheap commercial gas sensors. All of these systems have huge numbers of variables, and we need to utilise “big data” approaches combined with AI/Machine Learning to characterise and model them.

Machine learning, and more generally Artificial Intelligence, are finding wider applications in the field of Chemical/Process Engineering. The key challenge is to maintain physical correctness and validity of the trained models outside their region of training, thus ensuring safety, when embedding these models into process control, or process design. They must capture the underlying rules such as the laws of thermodynamics, and elemental constraints, thus we’re embedding the Machine Learning/AI at a higher level, layered above the thermodynamic and kinetic models.

The research you undertake here will focus on developing new process models using open-source process modelling frameworks. These can be existing modelling frameworks such as pyomo, or custom in-house solutions. You can work in close collaboration with any of our experimental projects, even taking over aspects of them, depending on your own interests and motivations. The project can be developed together with you; however, at its core will be the modelling work combined with modern engineering principles of rapid iteration and fail fast design which requires comparison against experimental results.

Prerequisites:

Selection will be made on the basis of academic merit. The successful candidate should have, or expect to obtain, a UK Honours degree at 2.1 or above (or equivalent) in Chemical Engineering, physics, chemistry, or a related field with computational modelling experience modelling.

Knowledge of:

Any background in process modelling and a programming language such as python, Julia, or C++ is highly desirable.

APPLICATION PROCEDURE:

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

• Apply for Degree of Doctor of Philosophy in Engineering

• State name of the lead supervisor as the Name of Proposed Supervisor

• State ‘Self-funded’ as Intended Source of Funding

• State the exact project title on the application form

When applying please ensure all required documents are attached:

• All degree certificates and transcripts (Undergraduate AND Postgraduate MSc-officially translated into English where necessary)

• Detailed CV, Personal Statement/Motivation Letter and Intended source of funding

Informal inquiries can be made to Dr M Campbell Bannerman with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Postgraduate Research School.

References:

T. Hanein, J. L. Galvez-Martos, and M. N. Bannerman, “Carbon footprint of calcium sulfoaluminate clinker production,” J. Clean. Prod., 172, 2278–2287 (2018)

T. Hanein, MS.-E Imbabi, F. P. Glasser, and M. N. Bannerman (2016). Lowering the carbon footprint and energy consumption of cement production: A novel Calcium SulfoAluminate cement production process. In IGCMat 2016, Los Angeles.

Leverhulme centre: https://www.abdn.ac.uk/engineering/research/leverhulme-centre-for-doctoral-training-in-sustainable-production-of-chemicals-and-materials-625.php

Artificial Intelligence has made great strides and is increasingly used throughout industry and our society however there are significant limitations in terms of what it can achieve. For example, the majority of methods are black box in nature meaning that they cannot explain themselves, and this limits the usefulness of these approaches and also raises ethical concerns. Research at the University of Aberdeen on explainable AI techniques have resulted in some breakthroughs in this area in a variety of areas (such as textual data analysis, robotics, and traditional data mining), and this project will look to build on this cutting edge research work and to form part of the PhD team here. 

The focus of the PhD will be in the area of Autonomous learning and Automated AI. Popular machine learning and deep learning techniques, especially supervised algorithms, are not suitable for autonomous learning due to their overdependence on large amount of labelled data that are not always available or are expensive to acquire. 

In addition, popular methods in Artificial Intelligence and reinforcement learning techniques that are used in robotics applications are not explainable and are not transparent in their learning, resulting in the AI being unable to adapt to new situations that it should encounter. This research will therefore focus in developing autonomy for the AI methods, with some significant progress in this area already.

In addition, this project will focus on the task of automating data analysis tasks, particularly for numerical data, but the techniques developed could be applied to other domains such as textual data analysis. The data mining process currently requires human interaction and guidance throughout the process, and this project will look to exploit artificial intelligence techniques in order to allow the automation of the data analysis and feature extraction tasks.

This project will look at the development of autonomous and automated learning techniques inspired by biological learning processes, and will build on a track record of success in this area and will work with a team of other researchers in the area of autonomous and automated AI (refer to cited references for more information).

This work has great commercial value and will likely be of interest to companies in the data analysis field.

Prerequisites:

Selection will be made on the basis of academic merit. The successful candidate should have, or expect to obtain, a UK Honours degree at 2.1 or above (or equivalent) in Engineering, Physics, Computing Science.

Experience in:

•  Computer coding,

• algorithms,

• mathematics

• data analysis

APPLICATION PROCEDURE:

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

• Apply for Degree of Doctor of Philosophy in Engineering

• State name of the lead supervisor as the Name of Proposed Supervisor

• State ‘Self-funded’ as Intended Source of Funding

• State the exact project title on the application form

When applying please ensure all required documents are attached:

• All degree certificates and transcripts (Undergraduate AND Postgraduate MSc-officially translated into English where necessary)

• Detailed CV, Personal Statement/Motivation Letter and Intended source of funding

Informal inquiries can be made to Dr A Starkey with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Postgraduate Research School .

References:

• Abdul Aziz, A & Starkey, A 2020, 'Predicting Supervise Machine Learning Performances for Sentiment Analysis Using Contextual-Based Approaches', IEEE Access, vol. 8, pp. 17722-17733.[ONLINE] DOI: HTTPS://DOI.ORG/10.1109/ACCESS.2019.2958702

• Ahmad, AU & Starkey, A 2018, 'Application of feature selection methods for automated clustering analysis: a review on synthetic datasets', Neural Computing and Applications, vol. 29, no. 7, pp. 317-328.[ONLINE] DOI: HTTPS://DOI.ORG/10.1007/S00521-017-3005-9

• Starkey, A & Ahmad, AU 2018, Semi-automated data classification with feature weighted self organizing map. in ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery. Institute of Electrical and Electronics Engineers Inc., pp. 136-141, 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017, Guilin, Guangxi, China, 29/07/17.[ONLINE] DOI: HTTPS://DOI.ORG/10.1109/FSKD.2017.8392964

• Ezenkwu, CP & Starkey, A 2019, Machine Autonomy: Definition, Approaches, Challenges and Research Gaps. in K Arai, R Bhatia & S Kapoor (eds), Intelligent Computing: CompCom 2019, Proceedings. Advances in Intelligent Systems and Computing, Springer , Cham, pp. 335-358, Computing Conference 2019, London, United Kingdom, 16/07/19.[ONLINE] DOI: HTTPS://DOI.ORG/10.1007/978-3-030-22871-2_24

• Ezenkwu, CP & Starkey, A 2019, 'Unsupervised Temporospatial Neural Architecture for Sensorimotor Map Learning', IEEE Transactions on Cognitive and Developmental Systems.[ONLINE] DOI: HTTPS://DOI.ORG/10.1109/TCDS.2019.2934643