Seminar

• • Prof. Matthew Turner
  • Nancy Rothwell Building, 3A.002
  • Wednesday 8th of October 2025
  • 2:00 pm – 3:00 pm

Abstract

Some practical systems, such as lunar/martian landers are actuated by on-off thrusters which leads, effectively, to control signals which are coarsely quantised. A practical, but perhaps naive, approach to the control of such systems is to follow the anti-windup paradigm: design a linear controller ignoring quantisation and then design an anti-windup compensator to deal with the quantisation. However, while practically appealing, such an approach has a few theoretical issues. Firstly, for some systems, even local asymptotic stability is impossible to obtain and one must instead seek to enforce ultimate boundedness of the state. Normally, if one can establish asymptotic stability, L2 gain conditions of some sort hold “for free” under mild conditions, but this lack of local asymptotic stability prevents them being established. However, a closely related alternative measure of performance can be enforced and this seems to be useful. The seminar will introduce and discuss the above issues.

Biography

Matthew Turner received his PhD in Control Engineering from the Department of Engineering, University of Leicester in 2000 and remained there for a postdoc, before being appointed to a lectureship. He remained at Leicester for 20 years, after which he joined the School of Electronics and Computer Science, University of Southampton, where he is currently a professor and affiliated to the Cyber-Physical-Systems Research Group. His background is in robust control and his main research interests are the (i) application of robust control techniques to, mainly, aerospace systems; and (ii) the development and application of anti-windup compensators to systems with constraints. His work has been funded mainly by industry and he has worked with Leonardo (Westland Helicopters), MBDA (UK and France), QinetiQ, dstl and others. From 2006–2012 he designed anti-windup compensators for several flight test campaigns with DLR, Braunschweig to help suppress the tendency of a pilot to enter into pilot-induced oscillation. Recently he has become interested in adaptive and learning control systems, of various sorts, and their attendant foibles.

• • Prof. Yuhu Wu
  • Nancy Rothwell Building, 4A.003
  • Friday 3rd of October 2025
  • 1:00 pm – 2:00 pm

Abstract

The parallel myopic best-response adjustment is a strategy updating rule frequently employed in evolutionary congestion games. Adopting this rule, however, may not lead the game to the Nash equilibrium. In this talk, a novel strategy updating rule is proposed by combining the classical best-response adjustment and a time-varying inertia. In the proposed rule, we first consider a prediction mechanism that utilizes the frequency of the selected resource, derived from any length of memory in past stages, to predict the resource congestion vector for the next stage. Furthermore, we consider an inertia-based best-response dynamics for the players with time-varying inertia based on the predicted resource congestion vector so that the players can either jump to the corresponding likely best-response strategy or keep the previous strategy in the next stage. A routing problem is used as the numerical example to verify the validity of the results.

Biography

Yuhu Wu received the Ph.D. degree in mathematics from the Harbin Institute of Technology, Harbin, China, in 2012. He held a Postdoctoral Research position with Sophia University, Tokyo, Japan, from 2012 to 2015. In 2015, he joined the School of Control Science and Engineering, Dalian University of Technology, Dalian, China, where he is currently a Full Professor. His research interests are related to optimization, and game-based control theory and applications of control to Boolean networks, automotive powertrain systems, and unmanned aerial vehicles.

• • Dr. Alessandra Sciutti
  • Nancy Rothwell Building, 2A.014
  • Thursday 25th of September 2025
  • 11:00 am – 12:00 pm

Abstract

Human cognition offers a blueprint for building robots capable of truly meaningful interaction. Moving beyond efficiency and control, the next step is to embed memory, anticipation, and adaptability as core cognitive mechanisms. These capacities enable robots to interpret the non-verbal layers of human behavior—gestures, rhythms, postures, and subtle signs of intention or care—just as people intuitively do with each other. By perceiving and responding to such cues, robots acquire predictive power and can participate more naturally in social exchanges. At the same time, they must embody expressive signals that humans can readily understand, creating a two-way channel of communication. Placing cognition at the heart of embodied communication paves the way toward robotics that is collaborative, transparent, and attuned to the dynamics of human relationships.

Biography

Alessandra Sciutti is Senior Tenure Track Researcher and head of the CONTACT Unit at the Italian Institute of Technology (IIT). She holds a Ph.D. in Humanoid Technologies from the University of Genova (2010) and has held research appointments in the USA and Japan. In 2018, she was awarded the ERC Starting Grant wHiSPER, focused on joint perception between humans and robots. She coordinates the ERC POC Project ARIEL, has published over 100 papers, and serves as Chief Editor of the HRI Section of Frontiers in Robotics and AI. She is also Associate Editor for multiple journals, an ELLIS scholar, and co-chair of the IEEE RAS Technical Committee for Cognitive Robotics. Her research investigates the sensory and motor mechanisms underlying mutual understanding in human-human and human-robot interaction.

• • Prof. Bruno Siciliano
  • Nancy Rothwell Building, 3A.012
  • Tuesday 16th of September 2025
  • 1:30 pm – 2:30 pm

Abstract

This talk will present the underlying concepts of EndoTheranostics, a novel ERC Synergy Grant project aiming at revolutionizing the diagnosis and therapy (theranostics) of colorectal cancer (CRC), impacting the quality of life of millions of individuals. CRC represents a significant proportion of malignant diseases. Interventions are often carried out during the latter stages of development, leading to low patient survival rates and poor quality of life. In 2022 a European Commission report stated that “colonoscopy-based screening has higher sensitivity than testing for blood in stool, but it is less acceptable to participants”. At the same time, effective methods to treat polyps in the colon are limited. Current approaches are often associated with unsafe oncological margins and high complication rates, requiring life-changing surgery. EndoTheranostics will usher in a new era for screening colonoscopy, advancing the frontiers of medical imaging and robotics. A tip-growing or eversion robot with a sleeve-like structure will be created to extend deep into hollow spaces while perceiving the environment through multimodal imaging and sensing. It will also act as a conduit to transfer miniaturized instruments to the remote site within the colon for theranostics. With these capabilities, the system will be able to offer: (i) painless colon cleansing in preparation for endoscopy, (ii) real-time polyp detection and tissue characterization through AI-assisted multimodal imaging, (iii) effective removal of polyps by conveying a “miniature mobile operating chamber” equipped with microsurgical tools to the target through the lumen of the eversion robot.

Biography

Bruno Siciliano is professor of robotics and control at the University of Naples Federico II. He is also Honorary Professor at the University of Óbuda where he holds the Kálmán Chair. His research interests include manipulation and control, human–robot cooperation, and service robotics. Fellow of the scientific societies IEEE, ASME, IFAC, AAIA, AIIA, he received numerous international prizes and awards, including the recent 2024 IEEE Robotics and Automation Pioneer Award. He was President of the IEEE Robotics and Automation Society from 2008 to 2009. He has delivered more than 150 keynotes and has published more than 300 papers and 7 books. His book “Robotics” is among the most adopted academic texts worldwide, while his edited volume “Springer Handbook of Robotics” received the highest recognition for scientific publishing: the 2008 PROSE Award for Excellence in Physical Sciences & Mathematics. His team has received more than 25 million Euro funding in the last 15 years from competitive European research projects, including two ERC grants.

• • Seb Oakes & Shengshu Liu
  • Nancy Rothwell Building, 3A.012
  • Thursday 4th of September 2025
  • 1:00 pm – 2:00 pm

Abstract

The ALACANDRA project focuses on the advancement of nuclear source localisation methods, with the aim of improving capabilities in nuclear environments through the use of unmanned ground robots. Such applications can include regular and repeatable assessment of radiation levels in active nuclear facilities, as well as providing rapid, detailed radiation maps in the event of nuclear disasters. We will cover the current progress towards these goals, including a summary of the recent deployment at a reactor in Slovenia, and outline future research directions.

Biography

Not provided.

• • Prof. Yasuo Namioka
  • Nancy Rothwell Building, 3A.012
  • Wednesday 3rd of September 2025
  • 2:00 pm – 3:00 pm

Abstract

Despite the rapid progress of smart manufacturing, human workers remain essential. Realizing digital twins requires accurate measurement and analysis of workers’ actions and tasks, providing critical data to connect the physical and digital worlds. This presentation introduces the latest technological trends in worker monitoring for manufacturing environments. We will discuss the tools and methods used, the types of data collected, and how these insights are being applied—or expected to be applied—to improve manufacturing processes and support the development of human-centric digital twins.

Biography

Not provided.

• • Chris Blum & Daniel Johnson
  • Nancy Rothwell_3A.016 M&T
  • Thursday 7th of August 2025
  • 12:00 pm – 1:00 pm

Abstract

Not provided

Biography

Not provided

• • Prof. Fulvio Forni
  • Nancy Rothwell_3A.083 M&T
  • Thursday 17th of July 2025
  • 12:00 pm – 1:00 pm

Abstract

Passivity-based control is a cornerstone of control theory and robotics (energy shaping and damping injection, control by interconnection). However, design and tuning of passive controllers remain challenging. In this talk we will discuss the core principles of passivity-based control and its application to contemporary robotics challenges. We will explore the idea of the feedback controller as a virtual mechanism, analogous to the physical morphology of a robot, whose structure and tuning shape the robot behaviour. To optimize controller performance, we will introduce algorithms for efficient gain tuning. Furthermore, we will combine constrained optimization with data-driven techniques for new passivity-based control methods tailored to real-world robotic applications.

Biography

Fulvio Forni is a Professor of Control Engineering at the University of Cambridge. His research interests include nonlinear control and robotics, from fundamental research in feedback control theory, to application-oriented research in robotic and precision agriculture. He received his Ph.D. in computer science and control engineering from the University of Rome Tor Vergata in 2010, and held a postdoctoral fellowship at the University of Liege, Belgium (2011–2015). He actively contributes to the UK’s control and robotics community by serving in the management team of the EPSRC CDT in Agrifood Robotics ‘Agriforwards’ and by chairing the Early Career Researcher Group of the Automatic Control Engineering Network (ACE). He was a recipient of the 2020 IEEE CSS George S. Axelby Outstanding Paper Award.

• • Ana Campos
  • Nancy Rothwell_3A.019 M&T
  • Thursday 3rd of July 2025
  • 12:00 pm – 1:00 pm

Abstract

From data-gathering protocols to the interpretation of statistical results, a good design is essential to allow objective and impartial conclusions when conducting experiments in any scientific field. This presentation will give an overview of good practices of design and analysis of experiments, using examples of past research in human-robot interaction.

Biography

Not provided.

• • Prof. Bing Chu
  • Nancy Rothwell_3A.083 M&T
  • Thursday 19th of June 2025
  • 12:00 pm – 1:00 pm

Abstract

Iterative learning control (ILC) is a design methodology for improving the tracking performance of systems that execute the same task repeatedly by learning from past actions. Originating from robotics research in the 1980s, a number of design approaches have been developed, many of which have been supported by experimental verification. This talk begins with a brief overview of ILC, then introduces a unified ILC framework based on the alternating projection method initially proposed by John von Neumann. This framework enables the derivation and analysis of a wide range of ILC algorithms. The talk will then present recent results on the application of this framework to the broiler production process. The connection between ILC and reinforcement learning will also be briefly discussed.

Biography

Bing Chu is a Professor in Electronics and Computer Science at the University of Southampton. He received his B.Eng. degree in Automation and M.Sc. degree in Control Science and Technology from Tsinghua University, Beijing, China, in 2004 and 2007, respectively. He completed his Ph.D. in Automatic Control and Systems Engineering at the University of Sheffield, UK, in 2009. Prior to joining the University of Southampton in 2012, he worked as a postdoctoral researcher at the University of Oxford from 2010 to 2012. He currently serves as Vice Chair of the IFAC Technical Committee 1.2 on Adaptive and Learning Systems, as an Associate Editor for the IEEE/ASME Transactions on Mechatronics and the IEEE Control Systems Conference Board, and as a member of the Editorial Board of the International Journal of Control. His research interests include iterative learning and repetitive control, the analysis and control of large-scale networked systems, applied optimization theory, and their applications in robotics, renewable energy, and next-generation healthcare.

• • João Moura & Diana Benjumea Hernandez
  • Nancy Rothwell_3A.016
  • Wednesday 11th of June 2025
  • 12:00 pm – 1:00 pm

Abstract

Diana: This project presents an architecture that combines autonomous control with a verified safety reasoning system, demonstrated through the AgileX Scout Mini robot inspecting a simulated nuclear facility. The objective is to show that the system consistently behaves as specified, ensuring the robot’s behaviour is predictable and free from unexpected or dangerous actions, making it suitable for UK nuclear sites.

João: Contact-rich manipulation, which involves intricate contact and force interactions with the environment, is crucial for enabling dexterous robotic capabilities. Non-prehensile manipulation tasks, such as pushing or catching objects, present unique challenges due to under-actuation, hybrid dynamics, and model uncertainty. This talk will explore both model-based trajectory optimization and model-free reinforcement learning approaches for addressing these challenges. Furthermore, whole-body manipulation can significantly enhance a robot’s reachability and mobility. However, the increased degrees of freedom and multiple contact points introduce additional complexities, including high-dimensionality and nonlinear dynamics. Advancing contact-rich whole-body manipulation holds great potential across various domains, from nuclear decommissioning and assistive healthcare to enhancing robots’ ability to support daily-life activities.

Biography

Not provided

• • Dr. Thulasi Mylvaganam
  • Nancy Rothwell_3A.002 M&T
  • Thursday 5th of June 2025
  • 12:00 pm – 1:00 pm

Abstract

In this talk we will consider two classes of dynamic optimisation problems, i.e. optimal control and differential games. The solution of such problems is characterised by nonlinear partial differential equations, which are challenging to solve in practice. We will consider two different approaches to systematically construct approximate solutions of dynamic optimisation problems, without requiring the solution of any partial differential equality. First, considering dynamic programming in the context of optimal control and differential games, we will see that it is possible to construct approximate solutions – with the level of approximation quantifiable (and, hence, minimisable) – based solely on immersion and algebraic conditions (in place of partial differential equations). Second, we will see that solutions of optimal control problems can be constructed by considering insights provided by dynamic programming and Pontryagin’s minimum principle simultaneously. Namely, exploiting properties of the Hamiltonian system associated with a given optimal control problem, approximate solutions can be characterised by (relatively simple) conditions involving trajectories of the Hamiltonian system.

Biography

Thulasi Mylvaganam received the M.Eng degree in Electrical and Electronic Engineering from Imperial College London, UK, in 2010. She completed her Ph.D. degree in 2014 in the Department of Electrical and Electronic Engineering, Imperial College London, UK, where she was a Research Associate from 2014–2016. From 2016–2017 she was a Research Fellow in the Department of Aeronautics, Imperial College London, UK, where she is currently a Senior Lecturer. Her research interests include nonlinear control, optimal control, game theory, distributed control and data-driven control. She is Associate Editor for the IEEE Control Systems Letters, the European Journal of Control and for the IEEE CSS Conference Editorial Board. She is also Vice Chair of Education for the IFAC Technical Committee on Optimal Control.

• • Prof. Chenguang Yang
  • Nancy Rothwell_3A.083 M&T
  • Thursday 22nd of May 2025
  • 12:00 pm – 1:00 pm

Abstract

Learning from Demonstration (LfD), or imitation learning, allows robots to acquire and generalize task skills through human demonstrations, creating a seamless integration of artificial intelligence and robotics. Most LfD approaches often overlook the importance of demonstrated forces and rely on manually configured impedance parameters. In response, my team has developed a series of biomimetic impedance and force controllers inspired by neuroscientific findings on motor control mechanisms in humans, enabling robots to imitate compliant manipulation skills. Our models reduce the dimensionality of skill representation, facilitating online optimization and reducing system sensitivity to parameter changes. To improve robot skill learning through enhanced perceptual capabilities, we designed anthropomorphic visual tactile sensors that assess contact force, surface texture, and shape, closely resembling the softness and wear resistance of human fingers for superior manipulation. The control and learning technologies we have developed have been particularly effective in robot teleoperation and human-robot collaboration, with shared control-based semi-autonomous methods that effectively integrate human intent with robotic autonomy, thereby achieving greater efficiency and usability.

Biography

Professor Chenguang Yang holds the Chair in Robotics in the Department of Computer Science at the University of Liverpool, UK, where he leads the Robotics and Autonomous Systems Group. He is a member of European Academy of Sciences and Arts, and he is also recognized as a Fellow by several prestigious institutions, including the Institute of Electrical and Electronics Engineers (IEEE), Institute of Engineering and Technology (IET), Institution of Mechanical Engineers (IMechE), Asia-Pacific AI Association (AAIA), and British Computer Society (BCS). Professor Yang serves as the corresponding Co-Chair of the IEEE Technical Committee on Collaborative Automation for Flexible Manufacturing (CAFM) and holds prominent editorial positions as Editor-in-Chief of Robot Learning, Senior Editor of IEEE Transactions on Systems, Man, and Cybernetics: Systems and IEEE Transactions on Automation Science and Engineering, Specialty Chief Editor for Computational Intelligence in Robotics for Frontiers in Robotics and AI. He previously served as President of the Chinese Automation and Computing Society in the UK (CACSUK) and has organized several conferences as the general chair, the 25th IEEE International Conference on Industrial Technology (ICIT) and the 27th International Conference on Automation and Computing (ICAC). As the lead author, he received the prestigious IEEE Transactions on Robotics Best Paper Award in 2012 and the IEEE Transactions on Neural Networks and Learning Systems Outstanding Paper Award in 2022.

• • Dr. Wasif Naeem
  • Nancy Rothwell_3A.083 M&T
  • Thursday 15th of May 2025
  • 10:30 am – 11:30 am

Abstract

In this research seminar, I will highlight our research on marine autonomous systems which includes development of guidance and control, path planning and collision avoidance of such systems. The focus of the talk will be on recent research advances in Autonomous Marine Vehicles, particularly, surface ships. The Rolls Royce led MAXCMAS project, and the Artemis Technologies led Hydrofoiling Ferries project will be reviewed as an appreciation of the complexities of such applications. The primary purpose of both projects is to improve the autonomy of such vehicles by developing intelligent path planning and collision avoidance capabilities. Standard collision regulations or marine ‘rules of the road’ will be explored whilst challenges will be discussed in codifying those rules, originally written for human consumption.

Biography

Dr Wasif Naeem is a Reader and Director of Internationalisation in the School of Electronics, Electrical Engineering and Computer Science at QUB and is also a member of the Centres for Intelligent Autonomous Manufacturing Systems and Energy, Power and Intelligent Control. His doctoral and postdoctoral research were both part of EPSRC-funded academia-industry collaborative projects to develop autonomous marine vehicles. Dr Naeem’s research has been funded by a variety of bodies including EPSRC, InnovateUK, UKRI, EU and the British Council. He is currently leading collision avoidance research for a hydrofoiling ferry as part of a £35M project led by Artemis Technologies. He was also the Principal Investigator from QUB on an £1.3M InnovateUK-funded Rolls Royce led MAXCMAS research project developing path planning and collision avoidance strategies for marine vehicles. As the coordinator of a €390k EU-funded Erasmus+ project, he led the development of training programmes in the area of Industry 4.0 related smart manufacturing and is currently leading a British Council project on the use of Generative AI tools in teaching, learning and assessment in higher education. He has authored over 105 peer-reviewed journal and conference papers and 3 book chapters. Two journal papers in the area of guidance of unmanned marine vehicles were awarded the Michael Richey Medal and IMarEST Denny Medal respectively by the Royal Institute of Navigation and Institute of Marine Engineering Science and Technology (IMarEST). Another conference presentation on the subject of COLREGs-based collision avoidance was awarded the ‘Most Innovative Research’ prize by the IMarEST. Dr Naeem has successfully supervised 12 PhD students in the general area of robotics. He is a member of the prestigious EPSRC College, a member of IFAC Technical Committee on Marine Systems and an Executive Committee member of the UK Automatic Control Council. He has been instrumental in bringing three international conferences to Belfast including the International Control Conference (Control2016) as the Local Organising Chair, 29^th International Irish Signals and Systems Conference (ISSC2018) as the General Chair and the IFAC International Conference on Intelligent Control and Automation Sciences (ICONS2019) as the Co-Chair. He sits on the Programme/Steering Committees and Associate Editor of a number of other international conferences.

• • Lanlan Su
  • Nancy Rothwell_3A.083 M&T
  • Thursday 24th of April 2025
  • 12:00 pm – 1:00 pm

Abstract

Part I: This presentation provides a benchmark example where a standard control system is modified with state-of-the-art safety guarantees implemented via a High-Order Control Barrier Function as safety filter. We will demonstrate that this modification ensures safety but induces unstable behaviour. In particular, we show that this instability is induced by a persistent switching between the original closed-loop dynamics and the CBF dynamics. Stability conditions will be needed to ensure that safety does not damage the stability of the closed-loop system.

Part II: In this talk, I’ll discuss how systems with different characteristics can still achieve synchronisation when connected in a network, even in the presence of nonlinear interactions. We focus on a property called passivity, which plays a key role in understanding how systems influence each other. The talk explores when a lack of passivity in some parts of the network can be balanced by passivity elsewhere—either in other systems or in the way they’re connected. I’ll present key results, simple examples, and a distributed condition that guarantees synchronisation in complex, heterogeneous networks.

Biography

• • Abdul Basit
  • Nancy Rothwell_3A.083 M&T. 
  • Thursday 10th of April 2025
  • 12:00 pm – 1:00 pm

Abstract

Transferring UAVs from simulation to real-world testing can be a time-consuming and frustrating process. One major challenge is the complexity of practical implementation, often unrelated to the novelty of the work itself. In this talk, I’ll share key best practices gained from nine months of building and testing UAVs, along with actionable recommendations. My hope is to show you that these practical considerations are not only worth your time but can also enhance your robots’ performance.

Biography

• • Alexander Rhoden
  • Nancy Rothwell_3A.083 M&T
  • Thursday 27th of March 2025
  • 12:00 pm – 1:00 pm

Abstract

In today’s world, RL-based learning-based methods are of great interest due to their adaptability to increasingly dynamic environments. With RL becoming ever broader as a methodology, what are the contrasting perspectives within the field? What are the key methods in use and development today?

Biography

Alexander is currently a 3rd-year PhD student in our group.

• • Dr. Sajjad Keshtkar
  • Nancy Rothwell_3A.012 M&T
  • Thursday 20th of March 2025
  • 1:00 pm – 2:00 pm

Abstract

The Space Dynamics and Control Laboratory at Tokyo Metropolitan University (TMU) conducts comprehensive research on spacecraft and aircraft dynamics and control, integrating theoretical, numerical, and experimental approaches. Our current work spans areas such as tether net systems, autonomous free-flying space robots and momentum exchange control moment devices. This presentation will focus on our latest projects, emphasizing advancements in autonomous space robotics and innovative control strategies for attitude control of spacecraft.

Biography

Dr. Sajjad Keshtkar is an assistant professor in the Department of Aeronautics and Astronautics at Tokyo Metropolitan University (TMU). He obtained his Bachelor’s and Master of Science degrees in aerospace engineering from the Kharkiv Aviation Institute, Ukraine, in 2009 and 2011, respectively. He earned his Ph.D. in Automatic Control from Cinvestav-IPN, Mexico, in 2016. His research interests include mechanism and machine design, as well as the automatic control of complex and nonlinear mechanical systems with applications in the aerospace industry.

• • Anthony Chen & Guido Herrmann
  • TBC
  • Thursday 13th of February 2025
  • 12:00 pm – 1:30 pm

Abstract

In this paper, we propose an adaptive critic learning approach for two classes of optimal pure state transition problems for closed quantum systems: i) when the target state is an eigenstate, and ii) when the target state is a superposition pure state. First, we describe a finite-dimensional quantum system based on the Schrodinger equation with the action of control fields. Then, we consider the target state to be i) an eigenstate of the internal Hamiltonian and ii) an arbitrary pure state via a unitary transformation. Meanwhile, the quantum state manipulation is formulated as an optimal control problem for solving the complex partial differential Hamilton-Jacobi-Bellman (HJB) equation, of which the control solution is found using continuous-time Q-learning of an adaptive critic. Finally, numerical simulation for a spin-1/2 particle system demonstrates the effectiveness of the proposed approach.

Biography

Anthony Siming Chen received his B.Eng. degree in Transportation Engineering from Central South University, China, in 2015. He then moved to the U.K. and obtained his M.Sc. and Ph.D. degrees in (Advanced) Mechanical Engineering from the University of Bristol in 2017 and 2022, respectively. Since January 2022, he has been a Postdoctoral Research Associate at the University of Manchester. His research interests include control systems, robotics, propulsion, and quantum systems

• • Anthony Chen
  • Engineering Building A_3A.059 M&T
  • Tuesday 5th of December 2023
  • 11:00 pm – 12:30 pm

Abstract

We propose a novel adaptive critic learning algorithm for a continuous-time nonlinear system subject to excitation and weight constraints. The algorithm is able to learn the optimal control in real-time under only finite excitation without requiring the a priori knowledge of the system model, i.e., the Hamilton-Jacobi-Bellman (HJB) equation is approximately solved online by the adaptive critic learning of a nonlinear Q-function.

The main contribution of this paper is twofold: First, we present an optimisation-based approach to the derivation of a weight-error-driven adaptive law that guarantees exponential convergence of the critic weight. Such formulation enables a new P-projection operator to enhance the convergence property, i.e., the weight estimate always stays in a bounded convex set that contains the true weight. Second, we adopt a new measure to build the information matrix that stores its richness over incoming data such that the standard persistent excitation (PE) condition is relaxed to a finite excitation (FE) condition. In this way, the convergence of the critic weight is guaranteed without persistently injecting exploration noise. We show that the method is model-free and can achieve semi-global stability. A numerical example demonstrates the effectiveness of the theoretical result.

Biography

Dr. Anthony Siming Chen received B.Eng. in Transportation Engineering from Central South University, China, in 2015. After moving to the United Kingdom, he studied at the University of Bristol with Professor Guido Herrmann as his advisor, and he received his M.Sc. and Ph.D. in (Advanced) Mechanical Engineering in 2017 and 2021, respectively. He was also a Visiting Researcher at the Institute of Advanced Propulsion Systems (IAAPS) at the University of Bath from 2015 – 2022.

From 2022, he has been working as a Postdoctoral Research Associate with the Department of Electrical and Electronic Engineering at the University of Manchester for RAIN+ and LongOps projects. His research interests include adaptive/optimal control, reinforcement learning, telerobotics, and automotive systems.

• Zhongguo Li
• Engineering Building B_2B.026 M&T
• Tuesday 21st of November 2023
• 13:00pm – 14:00pm



Abstract

Network-connected systems have received significant research attention over the past two decades, especially, in the domains of optimisation learning and control. Distributed algorithms have been developed aiming at facilitating decision-making at the local level while accomplishing certain global tasks through network communications.

The interaction between learning and control plays an important role in achieving intelligent operation for robotic systems in challenging environment. This presentation will focus on the recent advancement in the concept of reciprocal learning and control and its application in autonomous search problems.

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Biography

Zhongguo Li received both B.Eng. and Ph.D. degrees in electrical and electronic engineering from the University of Manchester, Manchester, UK, in 2017 and 2021, respectively. He is currently a Lecturer in Robotics, Control, Communication and AI with the Department of Electrical and Electronic Engineering at the University of Manchester.

Before joining Manchester, he was a Lecturer in Robotics and AI at University College London and a Research Associate at Loughborough University. His research interests include optimisation and decision-making for advanced control, distributed algorithm development for game and learning in network connected multi-agent systems, and their applications in robotics and autonomous vehicles.

• Tengyang Gong
• Engineering Building A_3A.018 M&T
• Tuesday 10th of October 2023
• 11:00 pm – 12:00 pm



Abstract

Backlash and saturation are two common nonlinearities in physical systems and may result in poor control performance. In previous studies, a novel control strategy based on Saturation Equivalence(SE) and Model Predictive Control (MPC) has been proposed for plants subject to both saturation and backlash at their input. This paper analyzes the performance of the control strategy under parameter uncertainty and presents recommendations for its implementation.

Simulation experiments illustrate the theoretical analysis and quantify the performance of the control strategy. The results show that it is better to underestimate than to overestimate both the backlash gap and the saturation level.

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Biography

Tengyang Gong received his MSc degree in Advanced Control and Systems Engineering from The University of Manchester in 2022, and his MSc dissertation was supervised by Prof. William P. Heath. He is currently a first-year PhD student in the control systems group at the University of Manchester, working on distributed optimization under the supervision of Prof. Zhengtao Ding.