جست‌وجوی استاد و پژوهشگر

Chris Freemanمشاهده پروفایل

استاد

• Iterative Learning Control
• Repetitive Control Theory
• Biomedical Engineering
• +۷ مورد دیگر

Chris Freeman is a Professor of Robotics and Control at the University of Southampton's Electronics and Computer Science (ECS) school. His research focuses on iterative learning control theory, biomedical engineering, and robotics with applications in industrial automation and healthcare. As Deputy Head of School (Equity, Diversity and Inclusion) and Chair of the ECS Belonging, Inclusion, Diversity and Equity (BIDE) Committee, he drives initiatives promoting inclusive academic environments. Freeman leads multidisciplinary research projects such as "Towards intelligent, pervasive, high performance control system architectures" "Elder Athletes: building incidental interaction at home" "Low-cost personalised instrumented clothing with integrated FES electrodes" . His work combines robotics, functional electrical stimulation (FES), and wearable technologies to develop rehabilitation systems for stroke patients and industrial automation solutions. His recent publications demonstrate expertise in iterative learning control (ILC), model predictive control, and biomedical applications. Research groups include: Digital Health and Biomedical Engineering Institute for Life Sciences Centre for Health Technologies Centre for Robotics

• Control Systems
• Convex Optimization
• Aerospace Systems
• +۷ مورد دیگر

Dr. Emmanuel Prempain is an Associate Professor at the School of Engineering, University of Leicester. His research focuses on control systems, convex optimization, and their applications in aerospace systems such as helicopters, re-entry vehicles, and UAVs. He specializes in robust control methodologies like gain scheduling, fixed-order synthesis, and Linear Matrix Inequality (LMI) frameworks. Recent work emphasizes energy-efficient control strategies for robotic arms and quadrotor UAVs, leveraging iterative learning control (ILC) and hybrid optimization algorithms. His contributions include model predictive control (MPC) designs for nonlinear systems and fault-tolerant switched control for multivariable systems. Key Technologies: Robust control, MPC, ILC, UAV control, aerospace systems Applications: Autopilot design, robotic trajectory tracking, energy optimization Dr. Prempain has developed a 2DoF Twin Rotor MIMO system for educational and research purposes, demonstrating practical applications of advanced control theories. His publications span over two decades, reflecting a sustained commitment to advancing control system methodologies.

Sebastian Stemmlerمشاهده پروفایل

پژوهشگر

• Control Engineering
• Industrial Automation
• Model Predictive Control
• +۷ مورد دیگر

Dr.-Ing. Sebastian Stemmler is a Managing Chief Engineer and Head of Industrial Systems and Production Systems at the Institute of Automatic Control (IRT), RWTH Aachen University, within the Faculty of Mechanical Engineering. He is actively engaged in research and development in industrial automation and control systems. His research interests focus on advanced control methodologies in industrial settings, particularly: Machine learning in industrial control engineering Control systems for digital and self-optimizing production systems Model-based Predictive Control (MPC) Iterative Learning Control (ILC) His recent publications (2021–2025) demonstrate a strong trend in applying MPC, ILC, and data-driven methods to injection molding, milling, and weaving processes. These works emphasize precision control, process optimization, and adaptation to material variability, especially with recycled plastics. His research bridges theoretical control strategies with practical industrial applications. His scientific achievements include being part of the research group that received the 2024 Cold Rolling Future Prize . He has co-authored numerous high-impact journal and conference papers, often in collaboration with leading researchers like Dirk Abel and Christian Hopmann. Stemmler supervises and collaborates on various industrial research projects, contributing significantly to advancements in smart manufacturing. He is involved in initiatives such as innocam.NRW and has contributed to research on AI in production and patient care. His work is supported by strong industry and academic collaborations. He leads the Industrial Systems and Production Systems research group at IRT, focusing on developing and implementing intelligent control solutions for complex manufacturing systems.

Hans Butlerمشاهده پروفایل

استاد

• Control Systems
• Lithographic Scanner Control
• Dynamics and Vibration Isolation
• +۳ مورد دیگر

Hans Butler is a part-time Full Professor in the Control Systems group of the Department of Electrical Engineering at Eindhoven University of Technology. He holds an MSc (1986) and PhD (1990) from Delft University of Technology. As an ASML Fellow, he specializes in dynamics and control systems with a focus on lithographic scanner control, particularly sub-nanometer positioning accuracy challenges. His work integrates physics-guided neural networks and advanced control strategies for mechatronic systems. Research interests include feedforward control design, vibration isolation, opto-mechatronics, and precision engineering applications. His contributions bridge academic research and industrial practice through collaborations with ASML, focusing on high-precision mechatronic systems used in semiconductor manufacturing. Butler has led teams in actuators development, system dynamics, and damping solutions at ASML since 1991. His recent work emphasizes data-driven methods and neural network implementations in control systems, supported by grants like NWO project 17973. He co-developed a MATLAB toolbox for physics-guided neural networks, fostering practical tooling for control engineers.

Lennart Blankenمشاهده پروفایل

استادیار

• Feedforward Control
• Learning Control
• Repetitive Control
• +۴ مورد دیگر

Lennart Blanken is a part-time Assistant Professor at the Mechanical Engineering Department of Eindhoven University of Technology (TU/e) and a researcher at Sioux Technologies (Mechatronics department) since 2019. His work bridges academia and industry, focusing on semiconductor and printing technologies. Education: MSc in Mechanical Engineering (cum laude) from TU/e, 2015 PhD in Mechanical Engineering from TU/e, 2019 His research specializes in advanced feedforward control, learning control, repetitive control, and system identification for mechatronic systems. Recent publications highlight frequency-domain methods, multirate systems, and kernel-based learning approaches to improve tracking performance in industrial applications. Advising & Ancillary Roles: Trainer at High Tech Institute. Grants & Collaborations: Funded by NWO (VIDI program 15698), ECSEL Joint Undertaking (IMOCO4.E), Horizon 2020, and JSPS-NWO programs (JPJSBP220234403, 040.040.060).

Xueqin Amy Liuمشاهده پروفایل

مدرس ارشد

• Smart Grid Data Analytics
• Renewable Energy Integration
• Energy Storage Systems
• +۳ مورد دیگر

Dr. Xueqin Amy Liu is a Senior Lecturer at Queen’s University Belfast (QUB) in the School of Electronics, Electrical Engineering and Computer Science. She holds a Ph.D. from QUB (2009) through a joint program with Zhejiang University. Her research bridges power systems and data analytics, focusing on smart grid challenges, energy storage, and renewable integration. She leads industry-driven projects like the €6.7M INTERREG SPIRE2 initiative, advancing energy storage solutions and grid stability. Dr. Liu is a Senior Member of IEEE and serves as an Associate Editor for IET Energy Conversion & Economics. She has supervised 13 PhD students and 2 postdocs, many employed in energy sectors. Notable achievements include the Best Graduate Student Poster Award (2021) and the Outstanding Associate Editor Award (2024). Education: Ph.D., Electrical and Electronic Engineering, Queen’s University Belfast (2009), joint with Zhejiang University Research Interests: Smart Grid data analytics, machine learning for grid stability, energy storage optimization, and renewable integration. Her work addresses real-world challenges with industry partners like EirGrid and SONI, developing tools for grid oscillation detection and energy market strategies. Recent Articles: Focus on oscillation mode identification, dynamic mode decomposition for grid analysis, and machine learning for electricity price forecasting. These contributions enhance grid stability and renewable integration. Awards: EPSRC Impact Acceleration Award (2022) Outstanding Associate Editor Award (IET, 2024) Best Graduate Student Poster Award (IEEE PES, 2021) Advising & Grants: Supervised 13 PhD students (e.g., Javier Lopez Lorente, Mark Rafferty) and led projects totaling millions in funding. Active in the SPIRE2 project and EU INTERREG programs. Labs/Teams: Her team develops diagnostic tools for grid oscillations and behind-the-meter analytics, collaborating with transmission system operators to enhance grid visibility and flexibility.

• Control engineering
• Robotics
• Machine learning
• +۶ مورد دیگر

Felix Kong is a Lecturer at the University of Technology Sydney, Faculty of Engineering and Information Technology. He specializes in control engineering, robotics, and mechatronic systems with a focus on maritime navigation and bio-inspired locomotion. Current affiliation : University of Technology Sydney (Lecturer since 2022) Previous role : Postdoctoral Research Fellow at University of Technology Sydney (2019-2022) Education : PhD from University of Sydney His research spans multiple domains: Maritime robotics : Stochastic ship routing, ocean current estimation, visibility graph optimization Dynamic locomotion : Gait analysis in parrots, bio-inspired vertical climbing robots Control theory : Iterative learning control, contraction analysis, SLAM optimality prediction Recent publications demonstrate a strong trend toward autonomous navigation systems that integrate: Stochastic weather modeling for ship routing 3D ocean flow estimation for underwater gliders Neural network-based SLAM analysis Multi-agent planning frameworks He has received funding from: Australia's Economic Accelerator Innovate Grant (2025-2027) UTS ECR Research Capabilities Initiative (2021-2022) Teaching contributions include course development and instruction for 41099 Fundamentals of Mechatronic Engineering . Peer-review activities extend to major robotics conferences and journals.

Kyriakos G. Vamvoudakisمشاهده پروفایل

استاد

• Reinforcement Learning
• Control Theory
• Cyber-Physical Security
• +۴ مورد دیگر

Kyriakos G. Vamvoudakis is the Dutton-Ducoffe Endowed Professor at the Daniel Guggenheim School of Aerospace Engineering and directs the Intelligent Cyber-Physical Laboratory. He holds a secondary appointment in the School of Electrical and Computer Engineering. His research integrates reinforcement learning , control theory , and game theory to develop secure and fault-tolerant autonomous systems, with applications in aviation, robotics, and critical infrastructure. His research focuses on: Safe autonomy for urban air mobility and cyber-physical systems Adversarial reinforcement learning to mitigate security threats Data-driven control for resilient multi-agent systems Bounded rationality in complex adaptive environments Recent publications emphasize adversarial robustness , safe RL , and cyber-physical security , with trends showing increased focus on real-time learning under uncertainty, deception in multi-agent games, and quantum control applications. He leads federally funded projects from NASA , NSF , ARO , and ONR , including: NASA ULI: Safety-Aware Learning for Aviation NSF CPS: Secure Assured Autonomy ARO: Non-Equilibrium Game-Theoretic Learning ONR Minerva: Cyber-Physical Situation Awareness At the Intelligent Cyber-Physical Laboratory , he oversees research on multi-agent coordination, secure autonomy, and reinforcement learning frameworks validated through real-world UAV and robotics platforms.

• Advanced Feedforward Control
• Learning Control
• Repetitive Control
• +۴ مورد دیگر

Lennart L.G. Blanken is a part-time Assistant Professor in the Control Systems Technology group at the Mechanical Engineering Department of Eindhoven University of Technology (TU/e). He also works at Sioux Technologies in the Mechatronics department since 2019, focusing on projects in the semiconductor and printing industries. His research emphasizes advanced control strategies including feedforward control, learning control, repetitive control, and system identification, with applications in mechatronic systems. Academically, he earned his MSc (cum laude) in Mechanical Engineering from TU/e in 2015 and his PhD in 2019, both under the supervision of Oomen, with a thesis on learning and repetitive control for complex systems applied to large format printers. His work has been published in journals like Meathctronics and Mechanical Systems and Signal Processing , focusing on frequency-domain methods, iterative learning control, and multirate systems. Key collaborations include projects with Philips Innovation Services, Océ Technologies (Canon Production Printing), and international researchers. His contributions span theoretical control development and industrial applications, with a focus on enhancing system performance through innovative control methodologies.

Andrew G Alleyneمشاهده پروفایل

استاد

• Control Theory
• Thermal Systems
• Manufacturing Automation
• +۴ مورد دیگر

Andrew G Alleyne is the Ralph & Catherine Fisher Professor in Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign (UIUC). He holds joint appointments in the Coordinated Science Laboratory and the Information Trust Institute. His academic roles include Director of the NSF Engineering Research Center (POETS) from 2015-2022, and he has served in leadership positions such as Associate Dean for Research (2008-2012) and Associate Head for Undergraduate Programs (2014-2015). Education: Ph.D. and M.S.E. in Mechanical Engineering from UC Berkeley (1994, 1992), B.S.E. in Mechanical and Aerospace Engineering from Princeton University (1989). Research focuses on control systems, thermal management, and mechatronics. Key areas include iterative learning control (ILC) for precision motion, transient thermal modeling for HVAC and power electronics, and additive manufacturing innovations. Applications span automotive, aerospace, and biomedical sectors. Publications highlight advancements in control theory, thermal systems, and interdisciplinary applications. Recent work emphasizes electrified mobility, hierarchical control frameworks, and nanoscale thermal devices. Over 200 peer-reviewed articles demonstrate contributions to both theoretical and applied engineering. Awards: ASME Henry Paynter Award (2014), IEEE Fellow (2017), Air Force Exemplary Civilian Service Award (2012) Grants and collaborations include leadership of the POETS ERC ($50M NSF-funded initiative). Mentoring over 50 graduate students and postdocs, with active roles in curriculum development and diversity initiatives. Serves on national committees for the NSF, ASME, and IEEE. Labs include the Alleyne Research Group focusing on mechatronics and control systems. Current projects address power density in electrified vehicles and advanced thermal management solutions.

• Control Systems
• Iterative Learning Control
• Robotics
• +۳ مورد دیگر

Professor Eric Rogers is a faculty member at the University of Southampton, specializing in Control Systems Theory & Design within the Electronics and Computer Science department. His research focuses on advanced control methodologies with applications across various domains including robotics, satellite systems, and medical rehabilitation technologies. Research Interests Professor Rogers' primary research areas include: Iterative Learning Control (ILC) theory and applications Control systems for robotics and autonomous vehicles Satellite formation flying control systems Medical applications of control systems, particularly for stroke rehabilitation Wind turbine control systems for renewable energy optimization Underwater vehicle navigation and control His work bridges theoretical control system design with practical implementation across diverse engineering domains, demonstrating strong interdisciplinary applications. Publication Trends Professor Rogers' recent publications (2021-2024) demonstrate a consistent focus on iterative learning control methodologies applied to increasingly diverse systems. His research has expanded from traditional control applications to include medical rehabilitation systems, wind energy optimization, underwater robotics, and satellite navigation. A notable trend is the increasing emphasis on experimental validation of theoretical control approaches, with several papers including detailed experimental results. His collaborative work spans multiple institutions and research groups, reflecting the interdisciplinary nature of modern control systems research. Research Funding Professor Rogers' research has been supported by significant funding from EPSRC (Engineering and Physical Sciences Research Council) across multiple projects including: Wrist Hand ILC project for stroke rehabilitation (with Professor Chris Freeman) Distributed Sensing projects (with Professor Andras Sobester and Professor James Scanlan) Constrained Control Methods for Autonomous Satellite Formation Flying Systems (with Professor Stephen Gabriel) Research England-funded SPRINT Innovation project (with Doctor Martyn Prince and Doctor Sandy Wright) Research Groups and Supervision Professor Rogers is an active member of the Vision, Learning and Control Centre for Robotics research group at the University of Southampton. He currently supervises three PhD students: Klevis Gasa (Electronics and Electrical Engineering), Andrew Stuart Macgillivray Begg (Defence and Security Mobility DTP), and Yuheng Deng (Electronics and Electrical Engineering), reflecting the interdisciplinary nature of his research.

• Complex intermittent process modeling
• Advanced process control
• Iterative learning control
• +۴ مورد دیگر

Jia SHI is an Associate Professor in the Department of Chemical Engineering and Biological Engineering at Xiamen University's School of Chemistry and Chemical Engineering. With a background in Control Science and Engineering, they have contributed to advanced process control techniques for batch processes over their academic career. Education: Bachelor's (Control Science, Xiamen University, 1994), Master's (Operations Research and Cybernetics, Xiamen University, 1997), Doctorate (Control Science and Engineering, Zhejiang University, 2006) Academic Career: Lecturer at Xiamen University's School of Information Science and Technology (1997-2007), Associate Professor since 2008 Their research focuses on complex intermittent process modeling and advanced process control techniques , particularly iterative learning control (ILC) and model predictive control (MPC). These methods address challenges in batch process systems through robust, adaptive, and intelligent control strategies. Selected recent publications demonstrate expertise in 2D modeling approaches for control systems, fault-tolerant control, and integrated feedback mechanisms. Key trends include application of mathematical optimization techniques to industrial chemical processes and development of robust algorithms for process reliability. As an active researcher, they have collaborated with institutions like Hong Kong University of Science and Technology (Research Assistant, 2003-2006) and maintain a strong publication record in top-tier journals such as AIChE Journal and Industrial & Engineering Chemistry Research.

Jim W. Portegiesمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Applied Mathematics
• +۵ مورد دیگر

Jim W. Portegies is an Assistant Professor in the Applied Analysis group at the Eindhoven University of Technology (TU/e). He holds additional roles as EAISI Foundational Assistant Professor and EAISI Mobility Assistant Professor within the TU/e's interdisciplinary initiatives. His research focuses on applying mathematical techniques from analysis, geometry, and measure theory to problems in machine learning and artificial intelligence, with a particular interest in designing algorithms that emulate human learning mechanisms. He obtained his MSc in Industrial and Applied Mathematics and Applied Physics from TU/e before completing his PhD in Mathematics at the Courant Institute of Mathematical Sciences, New York. After postdoctoral work at the Max Planck Institute in Leipzig, he returned to TU/e in 2016. Education: MSc in Industrial and Applied Mathematics & Applied Physics (TU/e, 2009) PhD in Mathematics (Courant Institute, NYU, 2014) Research Interests: Jim's work bridges pure mathematics and applied AI. Key areas include: Geometric and spectral methods for machine learning Universal learning algorithms inspired by human cognition Analysis of dimensionality reduction techniques Stochastic processes and control systems His recent work explores neural stochastic differential equations and interpretable AI mechanisms. Awards: Member of TU/e Young Academy of Engineering (2016–present) Collaborations & Grants: Jim collaborates extensively in interdisciplinary projects, including EU-funded initiatives on AI mobility systems. His research has been supported by grants from NWO (Netherlands Organization for Scientific Research) and industry partnerships with semiconductor and control systems companies. Labs & Teams: He contributes to the TU/e's Center for Analysis, Scientific Computing and Applications (CASA) and leads the Applied Analysis & AI research group, focusing on theoretical foundations of AI and their practical implementations.

Tom Oomenمشاهده پروفایل

استاد

• Control Systems
• System Identification
• Machine Learning
• +۷ مورد دیگر

Tom Oomen is a full professor in the Department of Mechanical Engineering at Eindhoven University of Technology. He specializes in control systems, system identification, and mechatronics, with applications in precision engineering, semiconductor technology, and healthcare. His research focuses on data-driven control strategies, integrating machine learning and artificial intelligence to enhance system performance. He has held academic positions at KTH Royal Institute of Technology, The University of Newcastle, and Delft University of Technology. Recipient of the 7th Grand Nagamori Award and NWO Veni/Vidi grants. Editor roles: Senior Editor of IEEE Control Systems Letters and Co-Editor-in-Chief of IFAC Mechatronics. Research interests include advanced motion control, iterative learning control, and fault detection. He teaches courses like Advanced Motion Control and organizes post-academic courses through the Mechatronics Academy. Collaborates with industries in semiconductor equipment, printing, space technology, and healthcare. Recent articles explore topics such as random learning in ILC, nonlinear control for ventilators, and gravitational wave detection systems. Advises PhD students including Max van Meer, Max van Haren, and Koen Classens.