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Our research on model predictive control (MPC) provides safe-by-design controllers for uncertain systems, with rigorous guarantees on safety and performance for general nonlinear systems. This includes the development of robust and stochastic MPC formulations that ensure safe operation under model uncertainty, such as uncertain parameters or stochastic noise. 

Selected talks & papers

• Tutorial:
• Sasfi, A., Zeilinger, M. N., & Köhler, J. (2023). Robust adaptive MPC using control contraction metrics. Automatica, 155, 111169.
• Köhler, J., & Zeilinger, M. N. (2025). Predictive control for nonlinear stochastic systems: Closed-loop guarantees with unbounded noise. IEEE Transactions on Automatic Control
• Dubied, M., Lahr, A., Zeilinger, M. N., & Köhler, J. (2026). A robust and adaptive MPC formulation for Gaussian process models. Automatica, 194, 113276.
  
• Köhler, J., Scholz, C., & Zeilinger, M. (2026). Robust reduced-order model predictive control using peak-to-peak analysis of filtered signals. European Journal of Control, 101569.

Our research focsuses on learning models from data and using it for control design. This includes offline identification of models and uncertainty quantification and online adaptation. 

Selected talks & publications

• ACE talk on

• Dubied, M., Lahr, A., Zeilinger, M. N., & Köhler, J. (2026). A robust and adaptive MPC formulation for Gaussian process models. Automatica, 194, 113276
  
• Degner, M., Soloperto, R., Zeilinger, M. N., Lygeros, J., & Köhler, J. (2026). Adaptive Economic Model Predictive Control: Performance Guarantees for Nonlinear Systems. IEEE Transactions on Automatic Control

• Köhler, J. (2026). Certainty-equivalent adaptive MPC for uncertain nonlinear systems. arXiv preprint arXiv:2603.17843.

This research bridges fundamental advances in control theory and machine learning to robotics applications, focusing on retaining theoretical safety guarantees while addressing limited on-board computation power. Key research directions include hierarchical tracker-planner architectures that separate long-term planning from short-term control, and learned neural network controllers trained with predictive control ideas.
We collaborate with international research groups to demonstrate our methods on robotics hardware platforms, including manipulators, drones, and wheeled robots. We are increasingly interested in collaborations to also move these methods to biomedical applications.

Selected papers

• Benders, D., Köhler, J., Niesten, T., Babuška, R., Alonso-Mora, J., & Ferranti, L. (2025). Embedded hierarchical mpc for autonomous navigation. IEEE Transactions on Robotics, 41, 3556-3574.
• Wullt, B., Köhler, J., Mattsson, P., Norrlöf, M., & Schön, T. B. (2026). Robust convex model predictive control with collision avoidance guarantees for robot manipulators. IEEE Transactions on Control Systems Technology.
• Köhler, J., Zhang, D., Soloperto, R., Carron, A., & Zeilinger, M. (2025). An MPC framework for efficient navigation of mobile robots in cluttered environments. arXiv preprint arXiv:2509.15917.
• Nubert, J., Köhler, J., Berenz, V., Allgöwer, F., & Trimpe, S. (2020). Safe and fast tracking on a robot manipulator: Robust MPC and neural network control. IEEE Robotics and Automation Letters, 5(2), 3050-3057.
• Tokmak, A., Fiedler, C., Zeilinger, M. N., Trimpe, S., & Köhler, J. (2025). Automatic nonlinear MPC approximation with closed-loop guarantees. IEEE Transactions on Automatic Control, 70(10), 6388-6403.

Autonomous driving is an emerging research direction for the group. We are interested in advancing the safety of autonomous vehicles, addressing vulnerable road users and integrating perception and control. We are part of ³Ô¹ÏºÚÁÏ’s Vehicles of the Future Hub, where we lead the autonomous vehicles theme.