ECR Webinar – AI meets Control
Date: Wednesday 12th February
Time: 10am – 1pm
Organiser: Dr. Zehor Belkhatir (University of Southampton)
The aim of this online event is to highlight the most recent research works at the intersection of control, Artificial Intelligence (AI), learning/data and their applications to real-life systems. The event will enable to connect researchers in general and early career researchers in particular who are interested in the interplay field between control theory and AI. The event will split in three sessions. The first two sessions will highlight the recent research advances at the intersection of control theory and AI. The speakers will showcase their theoretical and applicative research in the field. The last session will be dedicated to learning about industrial interests and problems requiring the hybrid field of control and AI. The session will also open-up discussions for follow-up potential collaborations between researchers having an interest in combining control theory with data and learning from AI to solve particular research problems.
Event Information
Open to
All
Cost
Free
Organiser
Automatic Control Engineering (ACE) Network
Location
This is an online event- Zoom link
Registration
Register at this link
Agenda
| Session 1 (1h 20min) | Research advances in Control, AI | |
| 10.00 – 10.10 | Welcome and introduction | Chair of the workshop:
Dr. Zehor Belkhatir (University of Southampton) |
| 10.10 – 10.30 (5 min QA) | Talk 1:
Data driven decision making and safety certificate synthesis |
Prof. Kostas Margellos
(Oxford University) |
| 10.30 – 10.50 (5 min QA) | Talk 2:
Application of set approximation in AI and engineering |
Dr. Morgan Jones
(University of Sheffield) |
| 10.50 – 11.10 (5 min QA) | Talk 3:
Learning full body deformation of soft robots using end-to-end models
|
Dr. Thomas George Thuruthel
(University College London) |
| 11.10 – 11.20 | Break | |
| Session 2 (60 min) | Rapid fire talks | |
| 11.20 – 12.00
5 minutes talks (3 min + 2 min Q/A) |
Cooperative control in swarm robotics
Learning Nash Equilibria via Innovative Dynamics Estimation and control of living neural circuits Online End-to-End Learning-Based Predictive Control for Microgrid Energy Management AI-Driven Approaches for Robust Control in Complex Robotic Systems Optimization for real-time grid operations Computation-aware set-based analysis and control. |
Dr. Junyan Hu
(Durham University) Dr. Leonardo Stella (University of Birmingham) Dr. Thiago Burghi (University of Cambridge) Dr. Francesca Boem (University College London) Dr. Zakaria Chekakta (University of Hertfordshire) Dr. Jack Umenberger (University of Oxford) Nikolaos Athanasopoulos (Queen’s University Belfast) |
| 12.00 – 12.10 | Break | |
| Session 3 (35 min) | Industry meets AI and control and panel discussion | |
| 12.10 – 12.30 | Data/AI & Control Meet Industry | Mr. Hector Figueiredo, CEng, MIET, Capability Lead – QinetiQ, Autonomous Systems |
| 12.30 – 12.50 | Round table discussion | All |
| 12.50 – 13.00 | Closing remarks | Dr. Zehor Belkhatir (University of Southampton) |
Biographies:
Kostas Margellos received the Diploma in electrical engineering from the University of Patras, Greece, in 2008, and the Ph.D. in control engineering from ETH Zurich, Switzerland, in 2012. He spent 2013, 2014 and 2015 as a postdoctoral researcher at ETH Zurich, UC Berkeley and Politecnico di Milano, respectively. In 2016 he joined the Control Group, Department of Engineering Science, University of Oxford, where he is currently an Associate Professor. He is also a Fellow of Reuben College and a Lecturer at Worcester College. He is currently serving as Associate Editor in Automatica and in the IEEE Control Systems Letters and is part of the Conference Editorial Board of the IEEE Control Systems Society and EUCA. His research interests include optimisation and control of complex uncertain systems, with applications to energy and transportation networks.
Dr. Thomas George Thuruthel is an Assistant Professor in the Department of Computer Science at University College London. He received his B.Tech. degree in Mechanical Engineering from the Indian Institute of Technology Hyderabad, India, in 2013, followed by a Master’s degree in BioRobotics from Waseda University, Tokyo, Japan, in 2015, and a Ph.D. degree with Honors from Scuola Superiore Sant’Anna, Pisa, Italy. He served as a postdoctoral researcher at the University of Cambridge from 2019 to 2022. His research interests include modeling and control, dexterous manipulation, and soft sensing. Currently, he is focusing on the control of soft robots, design optimization of soft-bodied systems, development of novel soft sensors, and dexterous manipulation using visuo-tactile information.
Morgan received the MMath degree in mathematics from The University of Oxford, England in 2016 and PhD degree from Arizona State University, USA in 2021. Since 2022 he has been a lecturer in the Department of Automatic Control and Systems Engineering at the University of Sheffield. His research primarily focuses on the estimation of reachable sets, attractors and regions of attraction for nonlinear ODEs. Furthermore, he has an interest in extensions of the dynamic programming framework to non-separable cost functions.
Junyan is an Assistant Professor with the Department of Computer Science at Durham University and a Fellow of Durham Energy Institute. Before joining Durham University, he was a Lecturer at University College London and a Postdoctoral Research Associate at the University of Manchester. His research interests include swarm intelligence, multi-agent systems, cooperative path planning, robust/adaptive control, with applications to autonomous vehicles and intelligent robotics. Junyan is a Senior Member of IEEE and a Fellow of Higher Education Academy. He serves as an Associate Editor for leading international journals and conferences in the field of robotics and control, e.g., IEEE Robotics and Automation Letters (RA-L), IEEE International Conference on Robotics and Automation (ICRA), and IEEE Conference on Control Technology and Applications (CCTA).
Leonardo Stella he is Assistant Professor in the School of Computer Science, University of Birmingham. He completed his PhD degree on the application of game theory and control for bio-inspired collective decision-making in 2019 from the Department of Automatic Control and Systems Engineering, University of Sheffield, United Kingdom. His research interests are game theory, control, reinforcement learning, and multi-agent systems. Applications include machine learning for process parameter optimisation in materials science and multi-agent reinforcement learning in swarm robotics.
Francesca Boem received the MSc degree (cum laude) in Management Engineering in 2009 and the PhD degree in Information Engineering in 2013, both from the University of Trieste, Italy. She was Post-Doc at the University of Trieste with the Machine Learning Group from 2013 to 2014. From 2014 to 2018, she was Research Associate with the Control and Power research group at the Department of Electrical and Electronic Engineering, Imperial College London, UK. From 2015 to 2018 she was part of the team at Imperial College working on the flagship EU H2020-WIDESPREAD-TEAMING project for the development of the EU KIOS Research and Innovation Centre of Excellence in Cyprus. Dr Boem was awarded the Imperial College Research Fellowship in 2018, and in the same year she has been appointed as a Lecturer in the Department of Electronic and Electrical Engineering at University College London, UK. She also is Honorary Lecturer at Imperial College London. In 2022 she has been awarded as PI the EPSRC New Investigator Award and a NNTI Joint Lab – Base Exploratory project in 2023. She currently is Associate Professor with the EEE Department at UCL and the Director of UCL’s MSc in Integrated Machine Learning Systems. Her current research interests include distributed fault diagnosis and fault-tolerant control methods for large-scale networked systems, safety and security of cyber-physical systems, learning-based control. Dr Boem is member of the IFAC Technical Committees 1.5 (Networked Systems) and 6.4 (“Fault Detection, Supervision & Safety of Technical Processes – SAFEPROCESS”) and Associate Editor for the IEEE Systems Journal, the EUCA European Journal of Control, the IEEE CSS Letters, and for the IEEE Control System Society, IFAC and EUCA Conference Editorial Boards.
Thiago B. Burghi is a post-doctoral research associate in the Control Group of the Department of Engineering at the University of Cambridge. In 2022, he received a 3-year Kavli Foundation research grant to study estimation and control algorithms for biological neuronal circuits. He completed his Ph.D on Control Theory in 2020 at the University of Cambridge. Previously, he received a M.Sc in Mechanical Engineering from the University of Campinas (Brazil), in 2015, and a double undergraduate degree in Engineering from ENSTA Paris and the University of Campinas, in 2014. His research interests lie at the interface between nonlinear control theory, system identification, and artificial and biological neural systems.
Dr. Zakaria Chekakta is a Lecturer in Robotics and AI Engineering at the University of Hertfordshire, bringing a wealth of experience in robotics research, teaching, and industrial applications. His academic journey began with a Ph.D. in Electronics and Industrial Control from the National Polytechnic School of Oran, Algeria, where he specialised in advanced optimal control for micro-UAVs. Complementing his doctoral studies, he holds an MS.c in Electronics and Embedded Systems and a BSc. in Electrical Engineering. Before joining the University of Hertfordshire, Dr. Chekakta served as a Research Fellow at City, University of London, contributing to cutting-edge projects in space robotics and multi-robot systems. His research focuses on applying artificial intelligence to robust control systems, cooperative robotics, and autonomous navigation. An active member of IEEE, he regularly reviews for esteemed journals and conferences in robotics and automation. Driven by a passion for innovation, Dr. Chekakta continues to advance research and education at the intersection of robotics and artificial intelligence.
Jack Umenberger is a Senior Research Fellow with the Department of Engineering Science and ZERO Insitute for zero-carbon energy research at the University of Oxford. Previously, he was a postdoctoral researcher at MIT and Uppsala University, Sweden. He attained his PhD from the University of Sydney, Australia on the topic of system identification.
His current research interests include optimization for control and decision-making, with a particular focus on data-driven or machine learning augmented approaches, and applications to energy systems (e.g. grid-scale control problems).
Nikos Athanasopoulos is a Senior Lecturer in Control, in the School of Electronics, Electrical Engineering and Computer Science at Queen’s University Belfast, Northern Ireland, UK. His research is in control theory and engineering, with a focus on hybrid systems and set-based methods for cyber-physical systems. He is the recipient of an IKY and a Marie Curie fellowship, and has held research positions in Eindhoven University of Technology, the Netherlands, and University of Louvain, Belgium. He obtained his Diploma and PhD from the Electrical and Computer Engineering Department in University of Patras, Greece.
Hector Figueiredo is a Chartered Engineer and Capability Lead at QinetiQ specialising in Autonomous Systems. He leads research teams in diverse autonomy related technologies being developed both in-house and externally which includes application of Systems Engineering principles to underpin research, development and exploitation. Commendations for his work have been received from DSTL, QinetiQ Innovation award and whilst at BAE Systems: Chairman’s Awards for Innovation, Silver and Bronze awards.
Relevant Technical expertise includes Autonomous systems, Crewed-Uncrewed teaming, Human autonomy teaming, aircraft design and performance.
Abstracts:
Title: Data driven decision making and safety certificate synthesis
Abstract: Data driven algorithms offer a natural framework to make decisions in environments affected by uncertainty, where uncertainty is represented by means of data. Neural networks constitute one class of such data driven decision making tools. However, the “learned’’ decisions are inherently random as they depend on the data used. In this talk we discuss how tools from statistical learning theory based on the notion of compression and randomized optimization offer a principled framework to analyze the robustness properties of these learned decisions. Our results build “trust on data’’, and accompany data driven solutions with probabilistic robustness
guarantees that capture their generalization properties when it comes to new data, not included in the learning/training process.
We review recent advancements in this area that allow to boost performance based on a data outlier removal procedure.
We then show how this methodology can be employed to build what will be referred to as safety informed neural networks, that produce safety and reachability certificates for nonlinear dynamical systems, accompanying them with prescribed probabilistic guarantees with respect to their validity.
Title: Learning full body deformation of soft robots using end-to-end models
Abstract: Learning-based techniques are becoming increasingly popular for developing models of soft robots for control purposes. Typically, they involve developing a mapping from the control space to the task space using real-world data. However, there have been limited works on using learning-based approaches for fully-body modelling of soft robots. This talk presents some of the latest work on learning full-body deformation of soft robots for static and dynamic cases, including interactions with the external environment.
Title: Application of Set Approximation in AI and Engineering
Abstract: This talk explores the application of set approximation techniques in AI and engineering, focusing on methods for approximating complex sets such as regions of attraction, reachable states, and feasible state spaces. Drawing from recent advances in sublevel set approximation using Hausdorff and volume metrics, we discuss numerical schemes based on Sum-of-Squares programming to approximate unions, intersections, Minkowski sums, and discrete sets. Applications in machine learning, such as one-class classification, and robotics, like collision-free path planning for Dubin’s car, illustrate the practical impact of these techniques in improving control theory and system design.
Title: Cooperative control in swarm robotics
Abstract: Recent advances in computing, communication, and control techniques, as well as increasing computational power of embedded systems, provide a great opportunity to deploy networked intelligent robots in complex tasks for higher accuracy, safety and efficiency. However, current demonstrations of robot swarms are mainly restricted to controlled or structured environments, which significantly limits their deployment in complex real-world applications. The main challenge in designing practical cooperative robotic systems is to determine the individual agents’ controllers that enable the collective to achieve the desired global objectives. This talk will briefly introduce some recent works on theory and applications of swarm control from Junyan’s group.
Title: Learning Nash Equilibria via Innovative Dynamics
Abstract: Despite its groundbreaking success, reinforcement learning still suffers from fundamental issues, including instability and non-stationarity. Replicator dynamics, the most well-known model from evolutionary game theory (EGT), provide a theoretical framework for the convergence to Nash equilibria in stable games. However, this is not true in other settings, e.g., null-stable games, where these dynamics exhibit periodic behaviours. In contrast, innovative dynamics, such as the Brown-von Neumann-Nash (BNN) or Smith, can directly converge to Nash without the need for time-averaging. In this talk, I will present our multi-agent reinforcement learning framework based on innovative dynamics and their potential for fast adaptation to non-stationary environments.
Title: Online End-to-End Learning-Based Predictive Control for Microgrid Energy Management
Abstract: A novel Online Learning (OL) algorithm is proposed, integrating Recurrent Neural Networks (RNNs) and Model
Predictive Control (MPC) in an End-to-End (E2E) learning-based control architecture, with application to the Microgrids energy management problem. The RNN predicts uncertain and possibly evolving profiles of electricity price, load demand, and renewable generation, which are then exploited in an integrated MPC optimisation problem to minimise the overall microgrid electricity consumption cost while guaranteeing operation constraints. To address the challenge of model uncertainty, a task-based loss approach is proposed by integrating the MPC optimisation as a differentiable optimisation layer within the neural network, allowing the OL architecture to jointly optimise prediction and control performance. The proposed methodology incorporates a specifically designed online version of the Stochastic Weight Averaging (O-SWA) and Experience Replay (ER) methods to enhance online learning capabilities, ensuring more robust and adaptive learning in real-time scenarios, as well as Transfer Learning (TL) capabilities.
Title: Estimation and control of living neural circuits
Abstract: Biological neurons have long been modelled in terms of nonlinear electrical circuits whose activity can in principle be regulated with the methods of control engineering. However, it was only recently that technological advances permitted us to meaningfully interact with living neural systems using real-time feedback control. Reliable closed-loop regulation of neural systems has the potential to revolutionize the treatment of neural disorders such as epilepsy and Parkinson’s, but achieving it poses major technical challenges. Neural systems are partially observed, nonlinear, adaptive and uncertain. In this talk, I will show how learning-based strategies combining LTI systems and artificial neural networks allow us to quickly obtain data-driven models of neural dynamics during an electrophysiology experiment. Using such models, I discuss ongoing work that aims to control neural dynamics by modulating the bifurcations underlying different regimes of neural activity. As a practical example, I discuss experiments in which such bifurcations are reliably triggered by changing the temperature of a neural central pattern generator.
Title: AI-Driven Approaches for Robust Control in Complex Robotic Systems
Abstract: Advancements in artificial intelligence and machine learning have opened new frontiers for robust control design in robotics and autonomous systems. However, the challenge of sim-to-real transfer remains a significant hurdle for deploying AI models in real-world environments, where uncertainties and dynamic interactions can degrade performance. In this talk, I will discuss AI-enhanced approaches to robust optimal control, focusing on integrating machine learning techniques with classical control strategies to enhance adaptability and resilience. I will share insights from my experience in developing AI-driven guidance, navigation, and control systems for space robotics and multi-robot systems.
Title: Optimization for real-time grid operations
Abstract: This talk will touch on recent advances in mixed-integer optimization for problems in which the underlying combinatorial structure can be represented by a graph, e.g. shortest path problems, with applications to real-time dispatch in electricity grids.
Title: Data/AI & Control Meet Industry
Abstract: Industry is keen to exploit Machine Learning/ Artificial Intelligence (ML/AI) enabled Systems for many reasons. To begin, they offer the potential to improve efficiency and productivity where cost reduction is always a driver. More importantly, for many Industries, removing humans from Dull, Dirty and Dangerous environments is also and often a higher priority. This presentation provides specific examples of Challenges experienced by Industry, which in reality extend to multiple domains. In the first application discussed, ML/Ai solutions are applied to control an underwater, ground moving vehicle to navigate over difficult terrain. In the Air domain, control of Uncrewed systems pose additional challenges. Proposed solution approaches exploit sensor – data fusion, localisation, navigation, threat avoidance, control and decision making for operations in dynamic and uncertain environments. However, to truly exploit these innovative solutions, Industry has to satisfy the Regulatory Authorities. The presentation seeks to discuss how challenges of trust, safety and assurance can be achieved.
Title: Computation-aware set-based analysis and control.
Abstract: Set-based methods are rich and versatile tools addressing challenges in analysis and decision problems. Although the theory is general enough to apply in almost every meaningful problem formulation, a straightforward implementation in analysis and control design inevitably hits computational complexity barriers. We note an alternative approach of computing invariant sets from a computational geometry point of view, changing the shape of polytopic invariant sets and preserve invariance at the same time. Moreover, we indicate application-oriented challenges eventually addressed by computing invariant sets and reachability set sequences, related to (i) robotics and autonomous, (ii) scheduling and resource allocation of computing networks and (iii) cybersecurity.
