The 30th International Conference on Automation and Computing (ICAC 2025)
Loughborough, UK, 27-29 Aug. 2025.
Keynote Speakers
Prof. Yang Shi
Fellow of EIC, IEEE, ASME, University of Victoria, Canada
Title: Adaptive and Learning Model Predictive Control for Dynamic Systems
Abstract: Model predictive control (MPC) is a promising paradigm for high-performance and cost-effective control of complex dynamic systems. This talk will report some recent results on adaptive and learning model predictive control (MPC) for a class of constrained dynamic systems with unknown model parameters. By proactively designing the online estimation mechanism and constructing the tube-based adaptive MPC scheme, the enhanced performance can be achieved compared to the robust tube MPC method. The application of adaptive and learning MPC to unmanned systems will be introduced. Some existing challenges and future research directions will be discussed.
Biography: Yang Shi received his B.Sc. and Ph.D. degrees in mechanical engineering and automatic control from Northwestern Polytechnical University, Xi’an, China, in 1994 and 1998, respectively, and the Ph.D. degree in electrical and computer engineering from the University of Alberta, Edmonton, AB, Canada, in 2005. He was a Research Associate in the Department of Automation, Tsinghua University, China, during 1998-2000. From 2005 to 2009, he was an Assistant Professor and Associate Professor in the Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, Canada. In 2009, he joined the University of Victoria, and now he is a Professor in the Department of Mechanical Engineering, University of Victoria, Victoria, BC, Canada. His current research interests include networked and distributed systems, model predictive control (MPC), cyber-physical systems (CPS), robotics and mechatronics, navigation and control of autonomous systems (AUV and UAV), and energy system applications.
On teaching and mentorship, Dr. Shi received the University of Saskatchewan Student Union Teaching Excellence Award in 2007, and the Faculty of Engineering Teaching Excellence Award in 2012 at the University of Victoria (UVic), and the 2023 REACH Award for Excellence in Graduate Student Supervision and Mentorship. On research, he is the recipient of the JSPS Invitation Fellowship (short-term) in 2013, the UVic Craigdarroch Silver Medal for Excellence in Research in 2015, the 2017 IEEE Transactions on Fuzzy Systems Outstanding Paper Award, the Humboldt Research Fellowship for Experienced Researchers in 2018; CSME Mechatronics Medal (2023); IEEE Dr.-Ing. Eugene Mittelmann Achievement Award (2023); the 2024 IEEE Canada Outstanding Engineer Award. He is IFAC Council Member; VP on Conference Activities of IEEE IES and the Chair of IEEE IES Technical Committee on Industrial Cyber-Physical Systems. Currently, he is Editor-in-Chief of IEEE Transactions on Industrial Electronics (2025/01-); he also serves as Associate Editor for Automatica, IEEE Transactions on Automatic Control, Annual Review in Controls, etc.
He is a Fellow of IEEE, ASME, CSME, Engineering Institute of Canada (EIC), Canadian Academy of Engineering (CAE), and a registered Professional Engineer in British Columbia, Canada.
Dr. Ning Wang
Associate Professor in Robotics and AI, Sheffield Hallam University, UK
Biography:
Ning Wang is an Associate Professor in Robotics and AI at the School of Computing and Digital Technologies, Sheffield Hallam University, United Kingdom. She received the BEng from the College of Automation, Northwestern Polytechnical University in 2005, M.Phil. and Ph.D. degrees from the Department of Electronics Engineering, The Chinese University of Hong Kong, Hong Kong, in 2007 and 2011, respectively. Dr Wang worked as a research fellow on machine learning and big data at the Dept. of Computer Science & Engineering, The Chinese University of Hong Kong (2011-2013) on physiological data mining and intelligent decision-making. She then worked on multimodal human-robot interaction (HRI) for elderly people with the Centre for Robotics and Neural Systems, University of Plymouth (2014-2015), under the support of EU FP7 Project Robot-Era. She has also been key member of EU Regional Development Funded Project ASTUTE 2020 (2018-2019) and industrial projects with UK companies. Dr Wang was a Senior Lecturer (2019-2024) and interim leader of teleoperation group at the Bristol Robotics Laboratory, the most comprehensive academic center for multi-disciplinary robotics research in the UK.
Dr Wang has been awarded several awards including Best Application Paper Award of ICAC’24, Best Paper Award of DISA’23, IET Premium Award for Best Paper 2022, etc. Her research seeks to address critical challenges at the intersection of robotics and intelligent systems, with applications in health & social care, industrial automation, and smart environments, etc. by offering solutions with human-like cognitive, social and physical capabilities in a machine.
Prof. Subramanian Ramamoorthy
Professor of Robot Learning and Autonomy, University of Edinburgh, UK
Biography:
Subramanian Ramamoorthy is a Professor of Robot Learning and Autonomy in the School of Informatics at the University of Edinburgh. He holds a UKRI Turing AI World-Leading Researcher Fellowship, and he is involved in multi-university initiatives including the UKRI AI CDT in Dependable and Deployable AI for Robotics and the AI Hub for Productive Research and Innovation in Electronics.
His research focus is on robotics and machine learning, with particular emphasis on achieving safe and robust autonomy in human-centred environments. This work has attracted funding from a variety of sources including UKRI, EU, DARPA, DSTL and the Royal Academy of Engineering, and been recognised with best paper awards at international conferences including ICRA, IROS, CoRL, ICDL and EACL.
In addition to his academic role, he has been involved in Five AI, a UK based technology company developing autonomous vehicles technology, as Vice President – Prediction and Planning (2017 – 2020) and Scientific Advisor (2021-23). Five AI was acquired by Bosch GmbH in 2022.
Prof. Shihua Li
Southeast University, China
Title: Recent Advances on Disturbance Rejection Control for Mechatronic Systems
Abstract:
For mechatronic systems, nonlinearities (frictions, backlash, saturation, etc.), complex internal dynamics, time-varying parameters, external disturbances and complex work tasks make control design a very challenging work. Compared with high gain control and integral control methods, disturbance estimation based control provides a different way to handle disturbance. Disturbance estimation based robust control method can effectively improve the disturbance rejection ability and ensure the robustness of closed-loop system. Some new research developments and results on this topic will be introduced. Specially we will discuss on various advanced modeling, analysis and disturbance rejection control techniques for mechatronic control systems with considerations of time delay, constraint safety control. Considering the characteristics of mechatronic control system, several kinds of composite control design schemes based on disturbance estimation and compensation are presented with experimental or application verification results.
Biography:
Shihua Li received his bachelor, master, Ph.D. degrees all in Automatic Control from Southeast University, Nanjing, China in 1995, 1998 and 2001, respectively. Since 2001, he has been with School of Automation, Southeast University, where he is a Chief Professor, dean of School of Automation, Jiangsu Province Specially Appointed Professor.
He is the chairman of IEEE IES Nanjing Chapter, Fellow of IEEE, IET, AAIA and CAA. He is an IEEE Distinguished Lecturer. He is also the Director General of Jiangsu Association of Automation. He served or serves as editor or associate editor of IEEE Transactions on Industrial Electronics, International Journal of Robust and Nonlinear Control, IET Control Theory & Applications, Advanced Control for Applications, etc.
His main research interests include modeling and nonlinear control theory with applications to mechatronic systems, autonomous unmanned systems. He has published 3 monographs, over 300 international journal and conference papers with 34000+ citations (Google Scholar). He is one of Clarivate Analytics Highly Cited Researchers all over the world in 2017-2024. He is a winner of the 6th Nagamori Award in 2020.
Dr. Dale Richards
Title: From Automation to Autonomy: A small step for machines, one giant leap for humans
Abstract:
Over the past 50 years we have seen how we have grasped the use of automation as a means to improve such things as safety and efficiency; perhaps using aerospace as a great example of this. However, this technology comes with a cost and is by no means without issue. When we fast forward to where we are today automation was merely the tip of the iceberg, providing the gate to advanced automation, autonomy and Artificial Intelligence. Again, while these technologies can provide increased benefit the nature of how the human interacts with it can still cause significant concern. In this talk we will explore how the human has been represented in the journey of how control has developed from deterministic automation to the bounded rationality of AI. And, form a Human Factors point of view, where do we go from here?
Biography:
Dr Dale Richards is a Chartered Psychologist and has both an academic and industry background. This has allowed him to bridge the gap between research and application in the field of AI and autonomous systems. Whilst at QinetiQ Dale led the Human Factors development of the first fast jet to control multiple UAS. Outside of his UK MOD work in autonomy Dale was also HF lead for QinetiQ for autonomous UAVs (ASTRAEA). Following this Dale joined academia for several years (Coventry and later Nottingham Trent University) where he taught Human Factors and won several research grants, ranging from self-driving cars to swarming UAS. Dale currently is a Principal Human Factors Engineer at Thales where he was HF Lead for the successful integration of the S100 UAV onto HMS Lancaster. He sits on the NATO WG for Human-Machine Teaming and has published many papers pertaining to the human element within autonomous systems.