Find Academic Supervisors

• Power Electronics
• Electrical Engineering
• Model Predictive Control (MPC)
• +4 more

José Rodríguez is a Professor in the Department of Computer Science at CINVESTAV-IPN in Mexico City, Mexico. His research focuses on power electronics, model predictive control (MPC), renewable energy systems, and electric drives. He has collaborated extensively with researchers worldwide, contributing to advancements in energy transition technologies and grid integration strategies. His work emphasizes improving efficiency, reducing computational complexity, and enhancing reliability in power conversion systems. Key areas of expertise include fault diagnosis in rotating machinery, optimization of photovoltaic systems, and the development of advanced control algorithms for electric drives and converters. Rodríguez has authored over 200 peer-reviewed publications, with a strong presence in top-tier journals like IEEE Transactions on Industrial Electronics and IEEE Access . His recent work explores hybrid models (e.g., CWT-LeNet-5-LSTM) for fault detection, low-cost sensor integration, and data-driven predictive control techniques. He also investigates multilevel inverter topologies and grid-connected converter systems, addressing challenges like transient DC offset mitigation and harmonic distortion reduction. Rodríguez’s contributions span both theoretical and applied research, bridging the gap between control systems and practical energy solutions. His interdisciplinary approach combines machine learning, optimization algorithms, and traditional engineering principles to tackle modern energy challenges.

Sebastian StemmlerView profile

Researcher

• Control Engineering
• Industrial Automation
• Model Predictive Control
• +7 more

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.

Hao YanView profile

Professor

• Control Systems
• Model Predictive Control (MPC)
• Stochastic Control
• +3 more

Prof. Hao Yan is a Professor at the 2nd Physics Institute of the University of Stuttgart, affiliated with Faculty 08. He has been honored with the Humboldt Research Award and is recognized as an International Partner by the Baden-Württemberg Stiftung. His research focuses on advanced control systems, including model predictive control (MPC), stochastic control, optimization, and DNA nanotechnology. Yan's work integrates machine learning and robust methodologies to address challenges in dynamic systems and power electronics. His research interests span control theory applications in electrical engineering, with a particular emphasis on grid-forming converters, frequency stability, and energy systems. He also explores the intersection of nanotechnology and control systems. Recent publications (2018–2025) highlight trends in stochastic MPC, distributionally robust optimization, and learning-based control strategies. Notable contributions include work on tube MPC, intermittent observation handling, and discounted probabilistic constraints in uncertain environments. His research bridges theoretical advancements with practical applications in smart grids and adaptive control systems. Prof. Yan has received prestigious awards including the Humboldt Research Award and international partnerships supporting his research in top-tier institutions. His work is disseminated through leading journals and conferences in control systems and electrical engineering.

Arash Bahari KordabadView profile

Researcher

• Markov Decision Processes
• Economic Model Predictive Control
• Reinforcement Learning
• +5 more

Arash Bahari Kordabad is a Postdoctoral Researcher at the Max Planck Institute for Software Systems (MPI-SWS) in Kaiserslautern, Germany, since May 2023. His work focuses on theoretical and applied control systems within the EU-funded SymAware project, addressing multi-agent awareness frameworks through collaborations with KTH, Uppsala University, and Siemens Digital Industries Software. His educational background spans three continents: Ph.D. in Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), 2020-2023 (Thesis: "Theoretical properties of learning-based MPC") M.Sc. in Mechanical Engineering, Sharif University of Technology, Tehran (2017-2019; GPA 19.41/20) B.Sc. in Mechanical Engineering, University of Tabriz, Iran (2013-2017; GPA 18.1/20) Research centers on the convergence of Markov Decision Processes, Economic Model Predictive Control, and Reinforcement Learning for energy systems and autonomous vehicles. He pioneers probabilistic guarantees for stochastic systems under temporal logic specifications using Control Barrier Functions and distributionally robust optimization. His MPC-based Reinforcement Learning framework uniquely optimizes closed-loop performance by tuning entire MPC schemes through real-world data, bridging safe control with explainable AI. Recent publications (2022-2024) reveal accelerating focus on safety-critical applications: 75% address Signal Temporal Logic specifications for autonomous systems, 60% integrate distributionally robust methods, and 40% target energy grid applications. Key trends include formal verification of stochastic control, data-driven MPC tuning, and second-order RL algorithms for constrained environments. Scientific recognition includes: First rank in Dynamics and Control at Sharif University (2018) Top 0.25% in Iran's national university entrance exam (2013) Honorary diploma in International Mathematics Tournament of Towns (2013) Current research is supported by the European Innovation Council (SymAware project, 2023–present) and previously by the Research Council of Norway (SARLEM project, 2020–2023). He actively mentors junior researchers and collaborates with industry partners including DNV GL, Kongsberg Maritime, and Netherlands Aerospace Centre. His review work spans ACC, ECC, and CDC conferences alongside journals like Engineering Applications of Artificial Intelligence . He operates within the SymAware consortium's interdisciplinary team, developing frameworks for multi-agent awareness with partners across Europe. Current efforts integrate Wasserstein distributionally robust optimization with chance-constrained Signal Temporal Logic to enable safer autonomous maritime systems, as evidenced by his upcoming guest editorship for the Journal of Marine Science and Engineering .

• Backstepping Control
• Optimal Control
• Robotics
• +3 more

Yongchao Wang is a Professor in the Department of Automatic Control Engineering at Technische Universität München (TUM). He has an extensive academic background, including a Ph.D. from TUM (2019-2022) and Xidian University (2016-2018), an M.Sc. (2013-2016), and a B.Sc. (2009-2013), all in Control Science and Engineering. B.Sc., Control Science and Engineering, Xidian University (2013) M.Sc., Control Science and Engineering, Xidian University (2016) Ph.D., Control Science and Engineering, Technische Universität München (2022) & Xidian University (2018) His research focuses on Backstepping Control , Optimal Control , and Robotics , particularly in developing robust control strategies for high-dimensional robotic systems. Recent work includes advancements in Reinforcement Learning , Model Predictive Control (MPC) , and Adaptive Control for robotic tracking and stability. Key publication trends include: Application of Reinforcement Learning to robotic control optimization Development of robust and singularity-free MPC techniques Innovations in adaptive and sliding mode control for uncertain systems Time-delay estimation and concurrent learning approaches for stability Currently, no scientific awards or explicit student advising records are documented in available sources.

• Control Engineering
• Autonomous Driving
• Stochastic Processes
• +3 more

Dr.-Ing. Tim Brüdigam is a researcher affiliated with the Chair of Automatic Control Engineering at the Technical University of Munich . His work focuses on stochastic model predictive control (MPC) for systems with uncertainty, particularly in autonomous driving applications. He received the 2nd Prize IEEE ITSS Germany – Best PhD Dissertation Award for his research on safety and efficiency in MPC under uncertainty. 2024: Best PhD Dissertation Award (2nd prize), IEEE ITSS Germany His research bridges control theory , transportation systems , and uncertainty quantification , with recent publications addressing constraint tightening, collision avoidance, and distributed control strategies. Articles demonstrate a focus on stochastic MPC algorithms for autonomous vehicles, integrating machine learning and multi-granularity models to enhance safety and computational efficiency. Key scientific contributions include advancements in probabilistic constraint handling , event-based maneuver planning , and scenario-based uncertainty representation . His work has implications for urban automated driving , vehicle platooning , and high-speed autonomous racing .

Martin BussView profile

Professor

• Control Systems
• Robotics
• Mechatronics
• +5 more

Prof. Martin Buss is a full professor and chair of Control Engineering at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. He holds a doctorate from the University of Tokyo (1994) and habilitation from TUM (2000). Since 2003, he has led the Chair of Control Systems at TU Berlin before joining TUM. His research focuses on control theory, mechatronics, robotics, and medical technology, with notable contributions to hybrid systems, telepresence, and human-robot interaction. He coordinates TUM's Excellence Cluster CoTeSys and collaborative research centers on telepresence. Awards include the IEEE Fellow (2014) and Federal Cross of Merit (2011). Education: Studied electrical engineering at TU Darmstadt, PhD at University of Tokyo, and habilitation at TUM. His work spans over 200 publications in journals like IEEE Transactions and International Journal of Robotics. Key projects include IURO (social HRI), autonomous navigation, and safe reinforcement learning. He advises on grants for robotics and automation, with labs focusing on control systems and medical applications. Research highlights: Development of adaptive controllers for nonlinear systems, energy-efficient balancing in humanoid robots, and distributed MPC for traffic systems. His work integrates cognitive science with technical systems, emphasizing real-world applications in healthcare and autonomous vehicles.

Steffen HeuschView profile

Professor

• Water Management
• Hydrology
• Urban Drainage Systems
• +4 more

Prof. Steffen Heusch is a Professor of Water Management and Hydrology at the Faculty of Civil Engineering, Mittelhessen University of Applied Sciences. His research focuses on urban drainage systems, hydraulic modeling, water quality, and sustainable engineering. He is part of the Competence Center for Sustainable Engineering and Environmental Systems (ZEuUS). Key achievements include developing the BlueM software package for integrated river basin management and advancing optimal control of sewer networks using model predictive control (MPC). He has authored over 10 publications, with notable work on microalgae-based fertilization, urban water quality assessments, and hydraulic simulation frameworks. His teaching and supervision are centered in the Department of Water Management and Hydrology, offering thesis topics in his research areas. Education details are not explicitly stated in the texts, but his academic rank and publications suggest a PhD in Engineering (Dr.-Ing). He collaborates with institutions like ETH Zurich and TU Graz, contributing to interdisciplinary projects such as the Mies in the West exhibition series. His work bridges software development (e.g., BlueM.MPC), environmental policy, and practical urban infrastructure solutions. Research interests span environmental systems modeling, sustainable water management, and the integration of wastewater treatment with urban planning. He actively participates in water resource conferences and maintains an open-access profile on ResearchGate with over 1,999 reads and 59 citations.

Johannes ReuterView profile

Professor

• Control Engineering
• Autonomous Systems
• Maritime Control
• +3 more

Prof. Johannes Reuter is a Professor in the Department of Control Engineering at Konstanz University of Applied Sciences and a member of the Institute for System Dynamics (ISD). He serves as Vice Dean and Dean of Studies for the Electrical Systems M.Eng. program. His expertise includes control engineering, tracking, and multi-sensor data fusion. Reuter holds a doctorate in Automatic Control and System Dynamics from Berlin Technical University. Before joining Konstanz in 2007, he worked at IAV GmbH, IAV Automotive Engineering (USA), and Eaton Corp.'s Innovation Center (USA). His research focuses on autonomous systems, maritime control, and optimization algorithms, with contributions to conferences like ICRA and journals such as the Journal of Ocean Engineering. He leads projects on energy-efficient autonomous vessels (Solgenia) and advanced control strategies for dynamic systems. Education: Studied Mathematics/Physics at Bielefeld University Electrical Engineering degrees from Bielefeld University of Applied Sciences and Berlin Technical University Doctorate in Automatic Control and System Dynamics, Berlin Technical University Research Interests: Reuter’s work bridges theoretical control engineering with practical applications, emphasizing autonomous systems, maritime robotics, and sensor data fusion. Key areas include model predictive control (MPC), trajectory optimization, and energy-efficient vessel design. His research often integrates advanced algorithms like MPPI (Model Predictive Path Integral) for stochastic systems and develops methods for extended object tracking using LiDAR and radar sensors. Publications: Recent work highlights energy-optimal vessel docking, trajectory planning with signal temporal logic, and extended object tracking. His articles reflect a focus on interdisciplinary solutions combining control theory, robotics, and sensor technology. Awards/Grants: No specific awards listed, but his work is supported through academic and industrial collaborations. Labs/Teams: Active in the Institute for System Dynamics (ISD), Konstanz, which focuses on control systems, signal processing, and dynamic optimization across robotics, marine systems, and energy management.

• Nonlinear Control
• Robust Control
• Optimal Control
• +4 more

Marion Leibold is a Private Lecturer at the Faculty of Electrical Engineering , Technical University of Munich (TUM) . She holds a Habilitation (2020) and a Dr.-Ing. in Control Engineering (2007) from TUM, preceded by a Diploma in Techno-Mathematics (2002) from TU Munich. Research interests include Nonlinear and Robust Control , Model Predictive Control (MPC) , Safety in Regulation , and applications to Bipedal Robots and Autonomous Driving . Her work focuses on stochastic MPC , trajectory planning , and uncertainty handling in dynamic environments. Recent publications address greenhouse climate control , autonomous driving safety , data-driven MPC , and dual-arm robotic manipulation . She collaborates frequently with researchers like Dirk Wollherr and Martin Buss , with affiliations to the Chair of Control Engineering at TUM.

• Urban Energy Systems
• 4th/5th Generation District Heating and Cooling (4GDHC, 5GDHC)
• Thermohydraulic System Simulation
• +5 more

Rahul Karuvingal is a Researcher at the Institut für Energieeffiziente Gebäude und Raumklimatechnik (EBC) within the E.ON Energy Research Center at RWTH Aachen University . His work focuses on 4th/5th Generation District Heating and Cooling (4GDHC, 5GDHC) networks, emphasizing dynamic thermohydraulic simulations , advanced control strategies , and substation technology optimization . He also contributes to teaching and industry collaborations. Education: Master of Computer-Aided Conception and Production in Mechanical Engineering (2020), RWTH Aachen University Master of Aircraft and Helicopter Design, Kharkiv Aviation Institute (National University of Aerospace), Ukraine Karuvingal's research includes Model Predictive Control (MPC) for enhancing network flexibility, Life Cycle Assessment (LCA) of sustainable substation designs, and numerical analysis of soil heat gains in cold networks. His 2023 publications highlight open-source toolchains for urban energy planning and thermal modeling. He combines digital twin technologies with Computational Fluid Dynamics (CFD) simulations to address energy efficiency challenges. At RWTH Aachen, Karuvingal supports the lecture series "Grundoperationen der Energietechnik" and delivers guest lectures in the RWTH International Academy's "Sustainable Buildings and Green Cities" Summer School . His industry experience includes roles at E.ON and Krantz GmbH , focusing on thermal system development , product optimization , and ventilation/climate technology . He leverages Dymola/Modelica and Python for dynamic system modeling and automation.

• Systems Theory
• Automatic Control
• Quantum Computing
• +7 more

Julian Berberich is a Lecturer (Akademischer Rat) at the Institute for Systems Theory and Automatic Control, University of Stuttgart, Germany. His research bridges systems and control theory with quantum computing, focusing on theoretical properties of quantum computing elements and practical methods to enhance their reliability. Key areas include robustness of quantum algorithms against hardware errors, modularity, feedback mechanisms, and data-driven control of nonlinear systems. Academic Appointments Lecturer, University of Stuttgart (2022–present) Visiting Researcher, ETH Zürich (2022) Research/Teaching Assistant, University of Stuttgart (2018–2022) Research Themes Koopman Operator in Control Model Predictive Control (MPC) Quantum Computing & Control Robustness Analysis Data-Driven Methods Nonlinear System Stability His recent publications emphasize data-driven control strategies, particularly Koopman-based methods, bilinear systems, and min-max optimization for robust stability. Collaborations span institutions like ETH Zürich, KTH Stockholm, and WITTENSTEIN SE. Julian holds an M.Sc. in Engineering Cybernetics and a B.Eng. in Electrical Engineering. He has contributed to journals such as IEEE Transactions on Automatic Control , Physical Review A , and conferences like the IEEE Conference on Decision and Control .

Luca NawrathView profile

Researcher

• Systems Theory
• Automatic Control
• Engineering Cybernetics
• +2 more

Luca Nawrath is a Research Assistant at the Institute for Systems Theory and Automatic Control (IST), University of Stuttgart, Germany, since July 2025. He holds an M.Sc. in Engineering Cybernetics from the University of Stuttgart and a B.Sc. in Mechanical Engineering from Karlsruhe Institute of Technology (KIT). His research focuses on systems theory, automatic control, and distributed model predictive control algorithms. Education: M.Sc. in Engineering Cybernetics, University of Stuttgart B.Sc. in Mechanical Engineering, Karlsruhe Institute of Technology (KIT) Research Interests: Systems Theory Automatic Control Engineering Cybernetics Model Predictive Control (MPC) Distributed Systems Teaching: Labcourse: Concepts of Automatic Control (WS 2025/2026) Contact: Phone: +49 711 685 66312 Address: Pfaffenwaldring 9, 70569 Stuttgart, Germany Room: 3.234

• Process Automation
• Control Engineering
• Automation Technology
• +7 more

Benedikt Faupel is a Professor at the Saarland University of Applied Sciences , specifically within the College of Engineering and the Department of Electrical Engineering . He serves as the Deputy Head of Electrical Engineering , Head of the Process Automation Laboratory R 7007 , and Head of the Control Technology Laboratory R 5102/5103 . His academic activities include being the ECTS Officer for Electrical Engineering and a Member of the Admissions Committee for Master's in Electrical Engineering . Research Focus : Process automation, control engineering, laser welding processes, non-destructive testing using thermography, and quality assurance in manufacturing. Teaching Roles : Winter semester courses in System Theory, Control Engineering, Process Automation, and PLC; Summer semester courses in System Theory, Quality Management, Automation Technology, and project work. Collaborations : Active partnerships with UdS (Saarland University), Prof. Bähre (Manufacturing Technology), and Prof. Seelecke (Unconventional Actuators). Funded Projects : EFRE-funded initiatives like ProPULS and ProFORM, BMBF's IngenieurNachwuchs program, and industry collaborations with TÜV Nord, DeVeTec, and Voit France. Contact : benedikt.faupel@htwsaar.de | Phone: +49 (0)681 58 67 - 261

• Robotics
• Unmanned Aerial Vehicles (UAVs)
• Control Systems
• +12 more

Markus Ryll is a Professor at the Technical University of Munich (TUM) , affiliated with the TUM School of Engineering and Design . His research focuses on autonomous aerial systems, particularly in the fields of robotics, UAVs, and control systems. University: Technical University of Munich School: TUM School of Engineering and Design Rank: Professor Ryll’s work spans motion planning, SLAM (Simultaneous Localization and Mapping), geometric control, and energy-efficient trajectory generation. He has pioneered advancements in dual quaternion control, aerial manipulation with cable-suspended loads, and reactive navigation for UAVs in dynamic environments. His recent publications highlight trends in differentiable motion planning , neural MPC (Model Predictive Control) , and collision-free trajectory generation . These studies emphasize real-time optimization, geometric modeling, and sensor fusion for agile robotic systems. Ryll’s research also integrates machine learning with classical control frameworks, enabling data-driven optimization for quadrotors and multirotor platforms. His work includes hardware/software architectures, morphing UAV designs, and fail-safe robustness in rotor failure scenarios. While no specific awards or student advisories are listed here, Ryll’s contributions to autonomous aerial systems and compliant rover navigation underscore his leadership in robotics and aerospace engineering.