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Jochem De SchutterView profile

Researcher

• Control Systems
• Renewable Energy Systems
• Optimal Control
• +5 more

Jochem De Schutter is a Researcher at the University of Freiburg's Laboratory for Systems, Control and Optimization. He holds a master's degree in Mechanical Engineering and Philosophy from KU Leuven, and completed his PhD in 2024 under Prof. Moritz Diehl. His research focuses on optimal control methods for energy systems, particularly airborne wind energy (AWE) and nonlinear periodic systems. Key areas of expertise include trajectory optimization, economic value analysis of AWE farms, robustness optimization, and efficient numerical methods for model predictive control (MPC). He has developed the AWEbox framework for simulating multi-aircraft AWE systems and contributed to tools like TuneMPC for MPC tuning. Teaching roles include lecturing on Model Predictive Control for Renewable Energy Systems (Master's, 2023) and serving as teaching assistant for Systems and Control courses since 2015. His work integrates advanced control theory with real-world energy applications, emphasizing scalability and computational efficiency. Research has been published in journals like Optimal Control Applications and Methods, IEEE Control Systems Letters, and Energies. He collaborates closely with institutions like KU Leuven and focuses on applied systems engineering for renewable energy solutions.

Alexander von RohrView profile

Researcher

• Robotics
• Machine Learning
• Bayesian Optimization
• +4 more

Alexander von Rohr is a postdoctoral researcher at the Technical University of Munich , affiliated with the Learning Systems and Robotics Lab . Previously, he was a doctoral researcher at the Max Planck Institute for Intelligent Systems and RWTH Aachen University , supported by IAV . His educational background spans Computer Science (RWTH Aachen), Electrical Engineering (BHT Berlin), and University of Lübeck . Research Focus : Embodied AI, Bayesian optimization, risk-aware reinforcement learning, robust control, probabilistic models, data-driven controller synthesis. His recent publications (2024-2025) emphasize event-triggered learning for time-varying systems, diffusion models with constraints , and robust safety via entropy regularization in RL. These works demonstrate applications in robot manipulators , digital twins , and underactuated systems . Awards include the Best Reviewer Award at AISTATS 2025 . Collaborations include Prof. Sebastian Trimpe and Angela P. Schoellig , with co-authorships on 15 recent articles. His lab focuses on probabilistic control for dynamical systems with formal guarantees.

Aamir AhmadView profile

Professor

• Aerial Robotics
• Multi-Robot Systems
• Deep Learning
• +4 more

Aamir Ahmad is a Tenure-Track Professor and holder of the chair 'Flugrobotik' (Flight Robotics) at the University of Stuttgart. He serves as Deputy Director (Research) at the Institute of Flight Mechanics and Control and leads the Flight Robotics and Perception Group (FRPG). Additionally, he is a Research Group Leader at the Max Planck Institute for Intelligent Systems (Perceiving Systems Department). His research focuses on aerial robotics, multi-robot systems, deep learning, and reinforcement learning applied to autonomous systems. Key research areas include formation control, aerial vision, and simultaneous localization and mapping (SLAM). He has published extensively on topics like reinforcement learning for multi-rotor landing, autonomous motion capture using aerial vehicles, and cooperative target tracking. His work often integrates theoretical advancements with practical applications in robotics, such as archaeological site mapping and environmental monitoring. Recent publications highlight trends in reinforcement learning for control systems, multi-agent coordination, and perception-driven robotics. Ahmad's group collaborates on projects funded by institutions like the European Union and German research networks. He actively mentors students and hosts research positions across all academic levels, emphasizing innovation in intelligent systems and robotics. His contributions have led to advancements in autonomous aerial systems, with applications in both academic and industrial settings. The FRPG team’s work has been showcased at conferences like IROS and ICRA, reflecting a balance between foundational research and real-world implementation.

Andreas GiengerView profile

Research Fellow

• Construction Robotics
• Data-based Modeling
• Fault Detection and Diagnosis
• +3 more

Dr. Andreas Gienger is a Junior Research Group Leader and Group Leader for Construction Robotics at the Institute for System Dynamics, University of Stuttgart, affiliated with the Cluster of Excellence IntCDC. He holds a Dr.-Ing. (Doctor of Engineering) degree and leads research in construction robotics, automation, and adaptive structures. Education: Master's in Technical Cybernetics, University of Stuttgart (2013–2016) Bachelor's in Mechanical Engineering, University of Stuttgart (2010–2013) Research Interests: His work focuses on data-driven approaches for robotics and structural systems, including fault diagnosis, optimization of large-scale manipulators, and cooperative control strategies. Key themes include adaptive structures, sensor integration, and real-time automation in construction. Publications: Recent articles (2020–2023) emphasize robotics applications in construction, fault-tolerant control, and data-based modeling. Trends include the integration of machine learning (CNNs, Bayesian methods) with traditional control systems for adaptive structures and industrial automation. Awards: None listed. Teaching: Courses include Trajectory Generation, Numerical Optimization, Simulation Technology, and Smart Manufacturing. He supervises projects in automation and process engineering. Labs/Teams: Leads the Construction Robotics group at the Institute for System Dynamics, collaborating on projects like the SFB1244 (adaptive structures) and IntCDC cluster.

David SchlipfView profile

Research Professor

• Wind Turbine Control
• Wind Farm Control
• Lidar Systems Optimization
• +3 more

David Schlipf is a Research Professor at the Wind Energy Technology Institute (WETI) within the Department of Energy and Biotechnology at Flensburg University of Applied Sciences, a position held since 2018. He also teaches wind energy fundamentals, control engineering, aerodynamics, and advanced mathematics at both Flensburg University of Applied Sciences and the University of Stuttgart. He founded sowento , a renewable energy startup, and holds leadership roles including Operating Agent of IEA Wind Task 32 (since 2016), Treasurer of the European Academy of Wind Energy (since 2017), and Editor at Wind Energy Journal (since 2020). His doctoral research at the University of Stuttgart focused on 'Lidar-Assisted Control Concepts for Wind Turbines'. Subsequent research at the University of Colorado Boulder and NREL specialized in floating wind turbine control. Research Focus: Schlipf's work spans wind turbine/wind farm control, lidar system optimization, and measurement data processing. Key areas include: Advanced control strategies (feedforward, model predictive) Lidar-assisted turbulence measurement and load mitigation Floating offshore wind dynamics International standardization (IEA Wind Task 32) Publications: His 15 most recent articles emphasize lidar integration for turbine/farm optimization. Predominant themes include feedforward control architectures, model predictive strategies for load reduction, floating turbine stabilization, and lidar data processing techniques. Field validations (e.g., CART3 platform, INNWIND.EU 10MW turbine) and international collaboration frameworks are recurrent elements. Leadership & Entrepreneurship: As Operating Agent of IEA Wind Task 32, he coordinates global R&D in lidar-assisted control. He co-founded sowento to translate academic research into renewable energy solutions.

• Model Predictive Control
• Stochastic Model Predictive Control
• Autonomous Driving
• +3 more

Tommaso Benciolini is a Research Associate and Ph.D. Student at the Chair of Automatic Control Engineering (LSR), Technical University of Munich, Germany. He holds a Master of Science in Automation Engineering from University of Padova, Italy, with a focus on Dynamical Systems and Control Theory, Robotics, and Machine Learning. His research spans control systems, autonomous driving, and uncertainty modeling, with a particular emphasis on trajectory planning and safety constraints. Education: Master of Science in Automation Engineering (University of Padova, Italy), Bachelor of Science in Information Engineering (University of Padova, Italy) His recent publications highlight advancements in: Stochastic Model Predictive Control (MPC) for autonomous vehicles Belief Function Theory for uncertainty fusion in traffic behavior estimation Fault diagnosis in dynamic systems Gaussian Process Regression for autonomous racing His work integrates robotics, machine learning, and control theory to address challenges in autonomous driving systems.

• Autonomous Vehicles
• Model Predictive Control
• Optimization
• +5 more

Ni Dang is a former Research Associate and PhD candidate at the Chair of Automatic Control Engineering (LSR) at Technical University of Munich (TUM), with a focus on advanced control strategies for autonomous vehicles. They earned a Master of Science in Circuits and Systems from Xidian University, China, and a Bachelor of Science in Electronic and Information Engineering from the same institution. PhD in Automatic Control Engineering (TUM, 2023) M.Sc. in Circuits and Systems (Xidian University, 2018) B.Sc. in Electronic and Information Engineering (Xidian University, 2015) Their research spans Autonomous Vehicles , Model Predictive Control , and Optimization , with interdisciplinary work in Epidemiology (2020) and Network Science (2018). Key contributions include applying Inverse Reinforcement Learning and Imitation Learning to model driving styles, integrating deep learning with control systems, and developing distributed stochastic MPC for traffic models. Their publications highlight trends in Autonomous Driving , Control Algorithms , and Network Analysis , balancing theoretical and applied approaches. A 2020 paper on a power-law model for pandemic spread demonstrates cross-domain adaptability of their methodologies.

Carsten W. SchererView profile

Professor

• Control Theory
• Robust Control
• Optimization
• +7 more

Carsten W. Scherer is a Professor and Head of the Institute of Mathematical Methods in Engineering, Numerical Analysis and Geometric Modeling at the University of Stuttgart, Faculty of Engineering. He holds the Chair of Mathematical Systems Theory and serves as Erasmus Coordinator for the Department of Mathematics. His research focuses on robust control, multiobjective control, linear matrix inequalities (LMIs), and semi-definite programming, with applications in mechatronics and flight control. He has authored numerous publications and contributed to advanced control theory methodologies. His research interests include exploring LMIs in control systems analysis, robust optimization techniques, and nonlinear control strategies. He has developed frameworks for model predictive control (MPC) and gain-scheduled control, leveraging integral quadratic constraints (IQCs) for system analysis and synthesis. Dr. Scherer’s work bridges theoretical advancements with practical applications, emphasizing convex optimization and its role in solving complex control problems. His contributions span both foundational theory and real-world implementations, particularly in aerospace and mechatronic systems. His publications reflect a sustained focus on robustness, optimization, and control system design, with recent work addressing data-driven methods, trajectory generation, and algorithmic synthesis. He leads research initiatives in mathematical systems theory and collaborates on interdisciplinary projects integrating control engineering with optimization and machine learning.

Johannes BetzView profile

Professor

• Autonomous Systems
• Vehicle Control
• Machine Learning
• +3 more

Prof. Dr. Johannes Betz is a Rudolf Mößbauer Professor at the Technical University of Munich (TUM), leading the Chair of Autonomous Vehicle Systems within the Department of Mobility Systems. His research focuses on software development for autonomous vehicles, emphasizing algorithms for environmental perception, path planning, and control to enable safe, efficient autonomous operation. He holds a B.Eng. and M.Sc. in Automotive Engineering from Coburg University and the University of Bayreuth, followed by a Ph.D. in Engineering from TUM (2019). Notable achievements include founding the TUM Autonomous Motorsport Team, which won the Indy Autonomous Challenge ($1M prize), and extensive postdoc work at TUM and the University of Pennsylvania. Education: B.Eng., Automotive Engineering, Coburg University of Applied Sciences (2013) M.Sc., Automotive Engineering, University of Bayreuth (2013) Ph.D. in Engineering, Technical University of Munich (2019) Research Interests: Autonomous vehicle software architecture Real-time trajectory and behavior planning Adaptive control for high-performance systems Simulation and validation frameworks Ethics of autonomous decision-making Awards: IEEE ITS Young Professional Travel Fellowship (2022) 1st Place, Indy Autonomous Challenge (2021) New Generation Star Project Award (2024) Outstanding Reviewer Award (2024) Labs & Teams: TUM Autonomous Motorsport Team (race competition leader) AVS Lab (Autonomous Vehicle Systems)

Rolf JohanssonView profile

Professor

• System and Control Theory
• Robotics
• Adaptive Control
• +13 more

Rolf Johansson is a Professor of Control Science at the Department of Automatic Control, Faculty of Engineering, Lund University, Sweden. He also serves as Director of the Robotics Laboratory and has held a dual affiliation with the Faculty of Medicine, Lund University Hospital, since 1987. He has held numerous visiting appointments at institutions including UC Berkeley, Caltech, Tsinghua University, and NTNU, reflecting his international stature in control systems and robotics. Research Interests: His research spans system and control theory , robotics , adaptive control , system identification , biomathematics , and automotive systems . He has made significant contributions to human balance and postural control , diabetes modeling , and industrial robotics . His work bridges engineering and medicine, particularly in applying control theory to physiological systems. Publication Trends: His recent publications focus on physical human-robot collaboration, learning-based control for engines and fuel cells, real-time trajectory generation using MPC, and sensorless force control in robotic assembly. These reflect a strong integration of machine learning, optimization, and robust control in both industrial and biomedical applications. Scientific Awards: IEEE Fellow (2012) Ebeling Prize (1995) ICRA2012 Best Automation Paper Award EURON Technology Transfer Awards (2004, 2007) Russell S. Springer Visiting Professor at UC Berkeley (2004) Fellow of the Royal Physiographic Society (2007) Advising and Grants: He has supervised over 25 PhD students across engineering and medicine. His research has been funded by major agencies including the Swedish Research Council (VR), EU Framework Programs (FP5–H2020), SSF, VINNOVA, and industry partners like ABB and Volvo. Key projects include SMErobotics, LCCC, DIAdvisor, and KCFP. Labs and Teams: He leads the Robotics Laboratory at Lund and has co-led the Balance Laboratory at Lund University Hospital. He is central to the LCCC (Linnaeus Center for Control of Complex Engineering Systems) and has coordinated multiple EU projects in robotics and control.

Sandra HircheView profile

Professor

• Control Engineering
• Cyber-Physical Systems
• Robotics
• +3 more

Prof. Sandra Hirche is the TUM Liesel Beckmann Distinguished Professor and Chair of Information-oriented Control at the Technical University of Munich (TUM). She leads the Professorship for Information-oriented Control within the TUM School of Computation, Information and Technology. Her research focuses on control engineering, system theory, and cyber-physical systems, with applications in robotics, human-machine interaction, and infrastructure systems. She holds a PhD from TUM and has conducted postdoctoral research at the Tokyo Institute of Technology and the University of Tokyo. Prof. Hirche coordinates the DFG Priority Program 'Cyber-Physical Networking' (2016–2023) and has held leadership roles in IEEE, including Vice-President of the IEEE Control Systems Society. Her awards include the Heinz Maier-Leibnitz Medal (2023), IEEE Fellow (2020), and ERC Consolidator and Starting Grants. She has authored numerous publications in top journals and conferences, focusing on topics like Koopman operator theory, Gaussian processes, and safety-critical control systems. Her research group explores networked control systems, data-driven methods, and human-centered robotics. Ongoing projects include the Konrad Zuse School of Excellence in reliable AI and the BMBF Research Hub 6G-Life. She actively mentors students and collaborates on interdisciplinary projects addressing societal challenges through control systems innovation.

Arno DentelView profile

Professor

• Renewable Energy Systems
• Building Energy Management
• Thermal Engineering
• +5 more

Prof. Arno Dentel is a Professor at Technische Hochschule Nürnberg, specializing in building energy systems, renewable energy integration, and predictive control strategies. His work focuses on optimizing heat pump systems, thermal energy storage, and HVAC efficiency through advanced control algorithms like Model Predictive Control (MPC). He has collaborated on projects such as SENSIBLE, exploring multi-scale energy storage solutions for grids, communities, and buildings. Research interests include: Photovoltaic (PV) integration into building facades Thermal mass activation for energy storage Experimental validation of simulation models using Hardware-in-the-Loop setups Weather forecast-driven control for heating systems Recent work highlights ventilation strategies in classrooms, MPC implementation for heat pumps, and long-term monitoring of building energy performance. He has published extensively on thermal comfort, phase change materials (PCMs), and building simulation tools like TRNSYS (Type 399). No scientific awards explicitly listed in provided texts. Advising activity details are not included in available information. His research contributes to decarbonizing building systems through smart energy management and renewable integration.

• Power Electronics
• Renewable Energy Systems
• Electrical Engineering
• +5 more

Frede Blaabjerg is a Professor at Aalborg University's Department of Energy Technology, Denmark. He specializes in power electronics, renewable energy systems, and grid integration. His research focuses on advanced control strategies for power converters, renewable energy integration, and smart grid technologies. With over 500 publications, he has contributed extensively to topics like inverter control, microgrid stability, and model predictive control. His work emphasizes practical applications in sustainable energy systems and high-efficiency power conversion. Key research areas include grid-forming inverters, fault-tolerant energy storage systems, and advanced modulation techniques for converters. Collaborations with industry and academia highlight his global impact. His studies often address challenges in integrating renewable sources into modern grids and improving system reliability through innovative control algorithms. He leads projects in wave energy conversion, smart metering, and hybrid AC/DC microgrids. His publications reflect a blend of theoretical advancements and engineering solutions, driving the energy transition through power electronics innovation.

Frank SchrödelView profile

Professor

• Automation Technology
• Robotics
• Control Engineering
• +3 more

Prof. Dr.-Ing. Frank Schrödel is a Professor of Drive, Automation, and Robotics Technology at Schmalkalden University of Applied Sciences' Faculty of Mechanical Engineering. His research focuses on control engineering, autonomous systems, Industry 4.0, and robotics applications such as social robots and autonomous driving. He leads training laboratories in Automation Technology, Drive Technology/Rapid Control Prototyping, and Robotics, as well as research labs in Industry 4.0 production systems and autonomous driving (in development). Prof. Schrödel holds a PhD in Control Engineering from RWTH Aachen University (2016) and a Diplom in System Technology from Otto-von-Guericke University Magdeburg (2010). His professional career includes roles as Team Leader at IAV GmbH (2016–2020) and Group Leader at RWTH Aachen's Institute of Automatic Control (2010–2016). His work bridges academic research and industry applications, emphasizing frugal innovations and model-based development. Research interests span robust control, model predictive control (MPC), and agile engineering processes. He actively develops educational infrastructure, including low-cost benchmark plants and open lab approaches for Industry 5.0 training. His publications address autonomous systems, collaborative robotics, and smart manufacturing, with a focus on practical implementation and interdisciplinary solutions. Current lab initiatives include nursing robotics (planned) and Industry 4.0 production systems. His work emphasizes human-centered technologies and digital transformation in mechanical engineering, reflecting a commitment to both academic rigor and real-world applicability.

• Control Systems
• Artificial Intelligence
• Mathematical Optimization
• +5 more

Giuseppe Carlo Calafiore is a Professor at the Polytechnic University of Turin, Italy, specializing in control systems, optimization, and their applications in finance, robotics, and machine learning. His work bridges theoretical advancements with practical implementations, focusing on stochastic systems, financial networks, and neural networks. He has contributed to methodologies for robust optimization, model predictive control, and distributed algorithms. Key research areas include system identification, energy systems optimization, and applications of control theory in epidemic modeling and financial contagion analysis. His research interests span control systems, artificial intelligence, mathematical optimization, and their intersections with financial engineering and robotics. Notable contributions include techniques for nonlinear system identification, robust resource allocation in telecommunications, and optimal clearing payment models in financial networks. He has also developed algorithms for distributed sensor networks and multi-agent systems, emphasizing scalability and reliability. Recent work explores sparse neural network architectures, federated learning convergence, and vaccination campaign planning under uncertainty. His interdisciplinary approach integrates tools from probability theory, convex optimization, and game theory, with applications in energy systems, robotics, and public health.