Research Supervisor Details

This page provides additional information about our research supervisors. You can either browser supervisors by department or search for them by keyword. Most supervisors also have a personal webpage where you can find out more about them.

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Dr Jonathan Aitken jonathan.aitken@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	My research interests lie in a broad collection of areas that focus around operation of autonomous robotic systems. My key research goals are to enable seamless operation of robotic systems in complex operating environments, whether this be: Manufacturing systems especially autonomous collaborative robotics, looking at systems architectures that provide flexibility across different tasks Developing digital twins and models of collaborative robots that enables the design of systems that faciliates manufacturing Operation of autonomous UAVs in normal traffic environments Trust in autonomous robot systems operating in public and maufacturing environments. Robots operating in underground pipe networks, especially focusing on navigation I have a collection of other research interests that I would be interested in developing further: Visual navigation, visual odometry in GPS-denied environments Optimisation of code related to autonomous robotic applications
Dr Sean Anderson s.anderson@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	Research interests: Identification of continuous- and discrete-time dynamic systems Nonlinear systems modelling Adaptive and optimal control in biological systems Neurorobotics Oculomotor plant dynamics Cerebellar function
Dr Mahnaz Arvaneh M.Arvaneh@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	Research Interests: Biomedical signal processing, machine learning and pattern recognition Statistical and adaptive signal processing, and mathematical modelling of bioelectric signals Neural and cognitive process, clinical applications, and understanding Brain–computer interface algorithms, systems, adaptation, and applications Robotic and BCI-based stroke rehabilitation Neuroprosthetic learning and control Medical system and device research and development Keywords: Automatic Control and Systems Engineering
Professor Michail Balikhin m.balikhin@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	Research interests: Space Plasma Turbulence in high beta (hot) plasma Collisionless Shocks Avalanching Systems Space Weather Solar-Terrestrial Relations Spacecraft Instrumentation Nonlinear Systems Identification of linear and non-linear processes in data Methods of data analysis for multi-spacecraft missions.
Dr Lin Cao l.cao@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	Dr. Cao’s main research interests include flexible endoscopic surgical robots, soft robots, and compliant robotic systems. Specifically, he develops novel design, actuation, sensing, modeling, control, and navigation principles of these systems that advance minimally invasive procedures, e.g., gastrointestinal flexible endoscopy, bronchoscopy, and catheterization. Collaborating with clinicians and industrial collaborators, he strives to develop flexible/soft robotics technologies that enable medical diagnosis and treatment with minimal invasiveness. These technologies are rigorously developed and tested, in both in-vivo animal trials and human trials, with the ultimate goal of making a real difference for the healthcare of patients.
Dr Dana Damian d.damian@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	Research Interests: My research group focuses on biomedical robotics, specifically bionics and capsule robots to advance healthcare technology for long-term therapies and non-invasive surgical interventions. We work on three main research themes: (1) soft-matter devices, in which we develop surgical and medical devices that are soft and functional such that they comply with the mechanics of soft tissue reducing inflammatory responses. Examples: soft sensors, soft pneumatic actuators, soft implants; (2) tissue-device interaction, in which we investigate advanced and efficient therapies based on in situ sensing. Examples: tissue patching, deployable miniature surgical devices, remotely controlled capsules; (3) resilient devices, in which we investigate methods and mechanisms to develop fault-tolerant devices that can continue their operation even in the event of a fault. Examples: mechanisms and control algorithms that avoid faults or detect and isolate the faults. Relevant background to carry out this research: mechatronics, bioengineering, electrical engineering, mechanical engineering, material engineering or chemical engineering. Keywords: Automatic Control and Systems Engineering,
Professor Sanja Dogramadzi S.Dogramadzi@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	Research Interests: My research in biomedical and assistive robotics includes both basic and applied topics. In biomedical robotics this includes surgical robotics - instrumentation, sensing and haptics for minimally invasive surgery; rehabilitation exoskeletons and intention sensing, surgical tele-operation and VR. In assistive robots this includes physically-assistive robots for assistance in dressing, sit to stand and walking and safe physical human-robot interaction. I am also interested in pattern recognition for solving complex fractures. Medical robotics applications in orthopaedic fracture surgery, minimally invasive surgery, radiology and brachytherapy.
Dr Ross Drummond ross.drummond@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	Research description: Dr Drummond's research has three main focus areas: the management and control of energy storage devices, nonlinear systems analysis and the robustness analysis of neural networks. A primary concern is the use of control theoretic techniques to optimise the performance of energy storage devices such as lithium ion batteries. This includes the design of fast charging protocols, model development and advancing battery manufacturing methods. The need to understand battery dynamics has motivated his research into nonlinear systems, in particular searching for novel Lyapunov functions. Finally, using these advances in nonlinear systems, he has been applying these methods to quantify the robustness of neural networks and relate them to control theoretic techniques such as model predictive control. Together, these three research streams emphasize how effectively utilising modelling, control and optimisation can improve the performance of several leading technologies such as batteries and neural networks.
Dr Inaki Esnaola esnaola@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	Research interests: My research interests include information theory and communication theory with an emphasis on application to electricity grid problems. My research focuses on understanding the fundamental limits governing systems with incomplete or mismatched system information. Today, we are seeing a growing amount of stored electronic data, and larger more diverse networks whose agents interact with limited information. However, many of the fundamental questions are still open. Tools from assorted communities such as information theory, probability theory, and random matrix theory among others, are proving useful but we are still lacking in our understanding of these systems and how to provide constructive guidelines for optimal algorithm design.
Professor Viktor Fedun v.fedun@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	Research interests: My research is primarily concerned with the mathematical modelling of physics of solar/space plasmas; sun-solar wind; solar-terrestrial systems. The study of processes occurring in such systems is crucially important for understanding the Sun, predicting Space Weather and understanding the dynamics of laboratory and technological plasmas. This includes mathematical modelling of solar magnetic flux tubes and processes that heat and maintain the coronal plasma at multi-million degree temperatures; studying fundamental plasma processes such as waves and instabilities in inhomogeneous media; determining the physical parameters of solar magnetic structures.
Dr Lingzhong Guo l.guo@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	Research interests: Identification of spatio-temporal systems and partial differential equations Frequency domain analysis of nonlinear infinite dimensional systems Proxy measurement, surrogate modelling, and model reduction Multiscale modelling of biomedical system
Mr Morgan Jones morgan.jones@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	My research interests are in the development and use of convex optimisation techniques to control and analyse nonlinear dynamical systems. I have particular interests in the stabilisation of power systems and optimal battery charge scheduling.
Professor Visakan Kadirkamanathan visakan@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	Research interests: My research interests belong to the broad category of signal and information processing. My research activities are partly in the Intelligent Systems, Decision and Control related research carried out within the Rolls-Royce University Technology Centre and partly in the Centre for Signal Processing and Complex Systems. They include both theoretical and applications research, and also external collaborations with other Sheffield Departments and Industries. The main research themes are: Modelling and Identification of natural and engineered complex systems Spatiotemporal system identification with applications in life, physical and social sciences Fault detection, diagnosis and prognosis with application to aircraft engines Intelligent systems decision support and applications in aerospace and biomedicine Autonomous and self-organised swarms and agent systems
Professor Zi-Qiang Lang z.lang@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	Research interests: Nonlinear system modeling, analysis and design in the frequency domain Health monitoring and fault detection of engineering systems and structures Smart structures and systems Wind turbine system condition monitoring and control Passive and semi-active vibration control with applications in marine, automobile, civil, and earthquake engineering Development of new healthcare technologies using complex system modelling and analysis approaches
Professor Mahdi Mahfouf m.mahfouf@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	Fundamental Research Fuzzy Logic, Fuzzy Sets, and Fuzzy Systems: modelling and Control (Decision Support Systems) Artificial Intelligence Self-Organising Fuzzy Logic Control Neural-Fuzzy Systems Machine Learning & Big Data Model-Based Predictive Control: Algorithms and Applications Evolutionary Based Optimisation- Single and Multi-Objective Application Areas Manufacturing: Materials, Surface Metrology, & Pharmaceuticals (in collaboration with Professor A.D. Salman from the Sheffield University Department of CBE) Human-Machine Interface (HMI) or Brain-Computer Interface (BCI), Operator Breakdown Transportation Systems Aerospace Systems Biomedicine: ICU, CICU, Neonates
Professor Lyudmila Mihaylova L.S.Mihaylova@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	Research interests: Broad research in the areas of signal processing, Bayesian methods, Monte Carlo methods, nonlinear estimation, target tracking, sensor data fusion, control, autonomous and complex systems (e.g. image and video processing, transportation systems, large scale systems) – both at theoretical and applied level.
Dr Andrew Mills a.r.mills@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	My research passion is to bring cutting edge technologies to application reality in complex environments through co-creation with Industry partners. Concrete examples include current partnerships with Rolls-Royce and Airbus which are seeing novel application of: Sensors: self powered wireless sensors running on engine testbeds, and RADAR/LiDAR systems for health monitoring and vision-based control; Data analytics: real-time to estimates of unmeasurable engine states, bring physics into data driven deep models for advanced anomaly detection; Control architectures: using systems based engineering to develop cyber-secure distributed control systems with multiple levels of safety criticality, fusing machine learning with control methodologies for increasing system resilience to faults and degradation. PhD topics in diverse areas are available including vision-based health monitoring systems for aircraft landing gear, generative AI for jet engine fleet forecasting, novel state estimation approaches using 'black-box' simulation models.
Dr Shuhei Miyashita Shuhei.Miyashita@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	My research interests reside in the following areas: Micro and milli robotics Self-assembling machines and 4D printing Meta-material Ingestible biomedical robots Origami robots Living machines and origin of life I have the following five technological aims: Micro/precision engineering Physical programmability Wireless operation Shape changing mechanism Distributed control
Dr Yuanbo Nie y.nie@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	Numerical Methods for Dynamic Optimization Dynamic optimization is integral to many aspects of science and engineering, commonly found in trajectory optimization, optimal control, state estimation, system identification and design synthesis problems. A key characteristic of dynamic optimization problems (DOPs) is that the decision variables can be functions or trajectories, leading to infinite-dimensional optimization problems that are often more challenging to solve. My current focus is on the development of a type of direct transcription method named the integrated residual methods. This is an excellent starting point to develop new DOP solution methods and next-generation software toolboxes. The advancements would allow DOPs to be formulated intuitively based on the problems' mission specifications and successfully solved thereafter, making the method easily accessible for scientists and engineers. Optimization-based Control Optimization-based control explores the use of optimization algorithms for feedback control of dynamical systems. For example, model predictive control (MPC) is a widely used optimization-based control method, allowing systematic and optimal handling of constraints, nonlinearities and uncertainties. The area I am particularly interested in is the design of optimization-based control with the optimization problem formulated directly based on the original problem specifications. Although such problems are typically more difficult to solve numerically, the difficulties are often offset by the availability of guarantees in solution properties, so that any local optimum solution (to a certain extent, even any feasible solution) can be considered suitable for real-world implementation. Control and Simulation of Aerospace Systems I have a strong interest in the control and simulation of aerospace systems, particularly when unconventional and counterintuitive solutions are needed. My current focuses are on Development of tool-chains that can be integrated into the aircraft's daily operations (e.g. as next-generation flight management systems), where optimal flight trajectories can be automatically obtained based on the information regarding aircraft aerodynamics, propulsion, departure and arrival airport, atmospheric conditions and any relevant air traffic control restrictions, Optimal energy management for electric, hydrogen and hybrid aircraft concepts, Multi-disciplinary optimal design of aerospace vehicles and flight control systems, for example, regarding the optimal sizing and placement of flight control surfaces, and the integration of distributed propulsion systems in flight control designs, Guidance and automatic control for the safe recovery of airliners in extreme conditions known as upset, such as stall and spin, Next-generation flight simulator concepts, e.g. ones that are suitable for upset recovery training
Professor George Panoutsos g.panoutsos@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	My research focuses on explainable and trustworthy machine learning (ML). Explainability is multifaceted in this context; I work on mathematical and computational methods in Computational Intelligence (CI) that enable enhanced understanding and transparent information use for neural networks, visual and numerical performance measures for many-objective optimisation algorithms, as well as linguistic interpretations of models, and safe control systems. Explainability and trustworthiness are key barriers in using machine learning in a range of critical applications, e.g. in engineering, and healthcare. A multitude of research questions still need to be addressed, for example how neural network - based systems learn and perform when information/data is imperfect, how can we exploit prior knowledge for enhanced learning, and how can we develop performance metrics that will allow us to understand the optimisation of systems at scale. Towards formulating research questions in machine learning, I often use challenge-driven research e.g. in manufacturing, healthcare, as case studies. This way, applications drive the research questions, towards maximising impact. I also use explainable machine learning for translational research and to create innovation to address global challenges (e.g. sustainability, energy). The advanced monitoring, optimisation and control of manufacturing processes is such an example, where ML-based methods can be used to reduce material waste, and minimise energy use. I welcome PhD applications in topics that fall under Computational Intelligence, in particular when these are concerned with explainable machine learning. Examples of recent PhD projects include, physics-guided neural networks, physics-guided generative models, new performance metrics for decomposition-based many-objective optimisation, information theoretic explainability in neural networks, safe reinforcement learning, and linguistic interpretations of Convolutional Neural Networks.
Dr Simon Pope s.a.pope@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	Research interests: Active and passive acoustic/elastic metamaterials (negative valued and tuneable density and/or modulus materials) Active control of sound and vibration (algorithms, control of vibration in remote structures, energy redistribution in actively controlled structures) Integration of next and future generation active materials into sound, vibration and fluid flow control Active and passive control of electromagnetic signals (includes metamaterials) Detection and removal of magnetic noise in space based scientific measurements Nonlinear structures in space and planetary plasmas
Dr Giuliano Punzo g.punzo@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	Keywords: Complexity, Consensus, Control theory, Network theory, socio-technical systems and game theory
Professor Robin Purshouse r.purshouse@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	Research interests: Robin's research aims to help improve how we identify and choose between possible solutions to a problem, with a particular focus on the process of policy appraisal. There are a number of factors that make policy appraisal a challenging research area: Multiple trade-offs Multiple stakeholders Deep uncertainty Cognitive challenge
Dr Anthony Rossiter j.a.rossiter@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	Research interests: His technical research has predominantly been based around the area of predictive control and more specifically with a focus on modifying the basic algorithm to optimise computational efficiency and/or simplicity with minimal sacrifice to the expected performance. Currently he is looking at how the algorithm, more normally used at a high level and requiring substantial computing power and set up costs, might be effectively deployed on microprocessors and other low level implementation technologies with minimal set up costs.
Dr Anton Selivanov a.selivanov@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	My research interests lie in the area of mathematical control theory. I study infinite-dimensional systems governed by partial differential equations (PDEs) and delay differential equations. My goal is to develop mathematical tools for designing controllers that guarantee the desired system behaviour in the presence of input/output delays, external disturbances, measurement noise, parameter uncertainties, and other phenomena occurring in practice. Research interests Control and stability of partial differential equations (PDEs) PDE-based analysis of multi-agent systems (robot swarms) Analysis and control of traffic flows Time delays and networked control systems Adaptive control
Dr Payam Soulatiantork p.soulatiantork@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	Industrial Automation Advanced control of Power converters for renewable energy systems Renewable energy real-time control and monitoring Programming and developing PLCs Supervisory, control and data acquisition (SCADA) Digital manufacturing and virtual commissioning Industry 4.0 and Cyber Physical Lab
Dr Lanlan Su lanlan.su@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	My research interests lie primarily in developing general control theory and mathematical tools for robust analysis & design of feedback systems and complex dynamic networks. The main research themes are: Robust control Multiplier-based and Dissipativity-based Analysis Networked Control Systems Large-scale Complex Dynamic Network Distributed Control/Optimisation Convex Optimisation and LMI I am also interested in developing and applying distributed control algorithms for a number of complex engineering tasks such as formation control, distributed estimation, resource allocation, and transportation network, wireless sensor network, etc.
Professor Ashutosh Tiwari a.tiwari@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	Professor Ashutosh Tiwari is Airbus/RAEng Research Chair in Digitisation for Manufacturing at the University of Sheffield. The vision of his research is to develop a digitised factory that requires no setups for manufacturing part variants (zero-setup) and no measurements on parts for ensuring quality (zero-measurement). Over the last eighteen years, he has developed three novel internationally recognised research themes to achieve this vision: (i) Digitisation of skill-intensive manufacturing processes, such as wing manufacture and engine assembly. His research is one of the first to focus on simultaneous digitisation of human actions and their impact on workpieces. (ii) Multi-level optimisation of manufacturing processes. He has developed new techniques for optimising the parameters of a manufacturing process at various levels (machine, multi-machine sequence, assembly and manufacturing system). (iii) Real-time simulation of manufacturing processes. His research has introduced the use of live shopfloor data to update factory simulation models in real-time.
Dr Paul Trodden p.trodden@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	Optimization and control
Dr Rob Ward r.a.ward@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	My main research interests are as follows: Digital Machining Machine Tool Control CNC Trajectory Generation & Interpolation Precision Motion Control Systems Robotic Machining Digital Twins Industry 4.0 Technologies Adaptive CNC Control
Dr Hualiang Wei w.hualiang@sheffield.ac.uk Personal Webpage Department of Automatic Control and Systems Engineering	Research interests: Identification and modelling for complex nonlinear systems NARMAX methodology and applications. Artificial neural networks (ANN), radial basis function networks (RBFN), wavelet neural networks and multiresolution wavelet models, computational statistics, machine learning, intelligent computation and data mining. Regression analysis, parameter estimation and optimization, sparse representation. Nonlinear and nonstationary (time-varying) signal processing, system identification and data modelling. Spatio-temporal system identification and modelling. Bioscience signal processing and data modelling Neurophysiology and neuro-imaging data modelling and analysis. EEG, fMRI and ECG data processing, modelling and analysis. Data based classification, pattern recognition, anomaly detection, with applications in clinical and medical diagnosis and prognosis. Forecasting and analysis of complex stochastic dynamical processes with applications in Space weather systems. Environmental systems. Computational economics and finance. New concepts and methodologies developments for the identification and analysis of nonlinear complex systems. Applications and developments of signal processing, system identification and data modelling to control engineering, bioengineering, neuroscience, systems/synthetic biology, environments, space weather and other emerging areas.
Professor Xin Zhang Xin Zhang Personal Webpage Department of Automatic Control and Systems Engineering	Research interests are in electrical power and energy systems, including power system control, planning and operation, smart grid and renewable energy, digital power systems, cyber-physical power system modelling and co-simulation, and transport electrification (land-air transport) with grid integration. The research areas that I am happy to supervise are: Power system planning, operation and control (transmission, distribution, microgrid) Cyber-physical system modelling, co-simulation, real-time digital simulation (Opal-RT, RTDS) AI, digital twins, machine learning applications to power and transport systems Power system with renewable and distributed energy resources (hydrogen, wind, solar, energy storage) Transport integrated power systems (electric vehicle / aircraft charging, energy systems for airport / transport hub)

Research Supervisors

Research Supervisor Details