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Jürgen AdamyView profile

Professor

• Control and Systems Theory
• Mobile Robotics
• Autonomous Systems
• +3 more

Prof. Jürgen Adamy is a Professor at Technische Universität Darmstadt, leading the Control Methods and Robotics lab since 1998. His research focuses on Control and Systems Theory, Mobile Robotics, Autonomous Systems, and Artificial Intelligence. He previously held engineering roles at Siemens AG (1992–1998) and was a Scientific Assistant at Universität Dortmund (1987–1991). His work integrates theoretical advancements with practical applications in robotics, energy systems, and autonomous vehicles. Research interests span nonlinear control, autonomous navigation, and multi-objective optimization. Notable contributions include PRORETA systems for urban driving safety and UAV path-planning algorithms. Recent articles emphasize remote driving performance, energy management, and bioengineering's role in societal progress. His lab develops technologies for smart grids, human-robot cooperation, and advanced control systems. Collaborations include Siemens and interdisciplinary projects like bio-engineering applications. He has advised numerous projects in robotics and control theory, though specific student names are not listed here.

Moritz DiehlView profile

Professor

• Optimization
• Control Systems
• Embedded Systems
• +3 more

Prof. Moritz Diehl is a Professor at the University of Freiburg, leading the Systems Control and Optimization Laboratory within the Department of Microsystems Engineering (IMTEK) and affiliated with the Department of Mathematics. Born in Hamburg, Germany, he holds a Ph.D. from Heidelberg University (2001) and previously served as a professor at KU Leuven (2006–2013), where he directed the Optimization in Engineering Center (OPTEC). His research focuses on optimization and control, emphasizing numerical methods for engineering applications, particularly embedded systems and renewable energy. Key areas include model predictive control (MPC), nonlinear optimization, and real-time control systems. Education: He studied physics and mathematics at Heidelberg University and the University of Cambridge (1993–1999), culminating in a Ph.D. in Scientific Computing. His academic journey includes roles at KU Leuven and Freiburg, where he has developed influential tools like the AWEbox framework for airborne wind energy systems and the acados optimization library. Research Interests: His work spans numerical optimal control, MPC algorithms, and their applications in robotics, energy systems, and automotive engineering. Recent advancements include collision-free motion planning, real-time NMPC with convex-concave constraints, and stochastic control methods for mobile robots. He also explores optimization for hybrid systems, leveraging finite elements and switch detection for nonsmooth dynamics. Publications: His 2023–2025 work highlights contributions to MPC stability, energy-efficient control systems, and software tools like LCQPow for quadratic programming. His research bridges theory and practice, addressing challenges in industrial processes, renewable energy integration, and autonomous systems. Labs & Teams: He leads the Systems Control and Optimization Lab, fostering interdisciplinary projects in optimal control, robotics, and sustainable energy. His group collaborates on tools like acados, emphasizing real-time feasibility and scalability for complex systems.

• Process Automation Systems
• Control Engineering
• Numerical Optimization
• +7 more

Sergio Lucia is a Full Professor (W3) for Process Automation Systems at Technische Universität Dortmund within the Department of Biochemical and Chemical Engineering since 2023. He previously served as a W2/W3 Professor (2020-2023) and W1 Assistant Professor at TU Berlin (2017-2020). His research focuses on the intersection of control engineering, numerical optimization, and machine learning, with applications in chemical processes, biotechnology, and energy systems. He leads the Laboratory of Process Automation Systems (Building G2, North Campus) and has held prestigious roles including Vice Chair of IFAC Technical Committee on Optimal Control since 2020. Education: Dr.-Ing. (summa cum laude) in "Robust multi-stage nonlinear model predictive control" (2014) Postdoctoral: Massachusetts Institute of Technology (2016), Otto-von-Guericke University Magdeburg (2015-2017) Alumni: Research Assistant at TU Dortmund (2010-2014), Diploma in Electrical Engineering (2010) His research explores novel methods to bridge theory and applications in control engineering, particularly through model predictive control (MPC) innovations. Recent work emphasizes robustness under uncertainty , Bayesian optimization , deep learning integration , and privacy-preserving federated learning for industrial applications. His 2025 publications address challenges in chemical recycling networks, crystallization processes, and serverless computing triggers. Scientific recognition includes: Teaching award (2023) Best student paper awards (2022, 2021) VAA Dissertation Award (2015) Erasmus Scholarship (2010) M.Sc. Extraordinary Career Award (2011) As a dedicated educator, he refines courses to enhance learning outcomes and mentors PhD students Sarah Braun and Benjamin Karg. His laboratory at TU Dortmund's North Campus is strategically located near the H-Bahn monorail system for accessibility.

• Control Engineering
• Numerical Optimization
• Machine Learning
• +6 more

Sergio Lucia is a Full Professor (W3) for Process Automation Systems at the Department of Biochemical and Chemical Engineering, TU Dortmund University. His research integrates control engineering, numerical optimization, and machine learning to address challenges in chemical processes, biotechnology, and energy systems. Education: Dr.-Ing. (summa cum laude) from TU Dortmund University (2014); Diploma in Electrical Engineering from University of Zaragoza (2010) Professional Journey: Full Professor (2023–present), Professor (W2) at TU Dortmund University (2020–2023), Assistant Professor at TU Berlin (2017–2020), Postdoctoral Fellow at MIT (2016) His recent work focuses on combining machine learning with model predictive control (MPC) for robust applications in chemical recycling, bioreactors, and energy networks. Key trends include AI-driven optimization, uncertainty quantification, and real-time control for complex systems. Scientific Awards Teaching award, TU Dortmund (2023) Best student paper award (PhD student Sarah Braun) (2022) Best paper by young author award (PhD student Benjamin Karg) (2021) VAA Dissertation Award for outstanding work in process engineering (2015) Erasmus Scholarship (2010) He has advised PhD students in chemical and biotechnological process optimization and led the Laboratory of Process Automation Systems at TU Dortmund's North Campus. His service includes Vice Chair of IFAC Technical Committee on Optimal Control (2020–present) and editorial roles in leading journals.

Tobias Kasper Skovborg RitschelView profile

Assistant Professor

• Control theory
• Model predictive control
• Stochastic adaptive control
• +9 more

Tobias Kasper Skovborg Ritschel serves as Assistant Professor (Tenure Track) in the Department of Applied Mathematics and Computer Science (DTU Compute) at the Technical University of Denmark. His research bridges theoretical advances in control systems with practical implementation across energy systems, bioreactors, epidemiology, and medical applications. Ritschel directs a productive research program focusing on computational methods for complex dynamical systems with rigorous thermodynamic foundations. His educational background demonstrates deep technical training: PhD in Applied Mathematics (2015-2018), Technical University of Denmark MSc in Mathematical Modeling and Computation (2013-2015), Technical University of Denmark BSc in Mathematics and Technology (2010-2013), Technical University of Denmark Ritschel's research expertise spans multiple domains of control theory and mathematical modeling. His core competencies include stochastic adaptive control, model predictive control, and optimal control frameworks applied to nonlinear dynamical systems. He specializes in numerical methods for differential equations (stochastic, partial, delay, and differential-algebraic) with computational implementations in MATLAB, C/C++, and Python. His application areas demonstrate remarkable breadth: from oil reservoir management and nuclear power systems to bioreactor operations, epidemiological modeling, and diabetes management technologies. Analysis of his publication trajectory reveals a strategic evolution from foundational work on thermodynamically rigorous reservoir simulation toward increasingly diverse applications. Early publications (2017-2019) focused on oil and gas applications with rigorous phase equilibrium modeling, while recent work (2020-2024) addresses pressing societal challenges including pandemic response strategies, power grid flexibility for renewable integration, and biomedical control systems. This demonstrates his ability to transfer core methodological expertise across disparate application domains while maintaining mathematical rigor. Dr. Ritschel actively supervises numerous students across all academic levels, with recent BSc projects focusing on molten salt reactors and demand-side flexibility in power systems. His teaching portfolio includes advanced graduate courses in stochastic adaptive control, dynamical systems, and time series analysis. He maintains strong industry connections through EU-funded projects including COCOP (Horizon 2020) and OPTION (Innovation Fund Denmark).

• Control Theory
• Model Predictive Control
• Data-Driven Control
• +5 more

Frank Allgöwer is a Professor and Head of the Institute for Systems Theory and Control Engineering at the University of Stuttgart's Faculty of Engineering. With an extensive publication record through 2025, he leads a prominent research group specializing in advanced control theory methodologies. His research interests span multiple domains of modern control theory, with particular emphasis on Model Predictive Control (MPC), data-driven control approaches, nonlinear systems analysis, and event-triggered control strategies. His work bridges theoretical foundations with practical implementations, focusing on stability guarantees, performance optimization, and computational efficiency. Recent research shows a strong focus on Koopman operator theory applications to control systems, distributed multi-agent coordination, and the integration of machine learning techniques with traditional control frameworks. Analysis of his 15 most recent publications reveals a consistent research trajectory centered around developing theoretically sound control methodologies with practical applicability. His work demonstrates increasing integration of data-driven approaches with traditional model-based control, particularly in the areas of nonlinear system control and distributed multi-agent systems. The publications span top-tier control journals including IEEE Transactions on Automatic Control, Automatica, and IEEE Control Systems Letters. Professor Allgöwer actively mentors numerous junior researchers, with frequent collaborations suggesting a strong supervisory role for PhD students and postdoctoral researchers. His group maintains productive international collaborations while being firmly rooted at the University of Stuttgart.