Vorträge

Model Predictive Control (MPC) is based on receding-horizon solution of optimal control problems and it is among the most successful advanced control methods. Core reasons are its applicability to nonlinear systems with constraints as well as the variety of tailored numerical algorithms and powerful software tools enabling
 efficient real-time implementations [1]. The application of MPC to cyber-physical systems (or to multi-
agent systems) is of pivotal interest in many application domains such as energy systems, logistics and
 transport, and robotics [2]. In this talk we present recent results on collaborative distributed nonlinear MPC 
for cyber-physical systems. We discuss a family of algorithms which is based on the decomposition 
of primal-dual Newton steps arising from Sequential Quadratic Programming (SQP) [3]. We explore 
how the underlying partially separable problem structure translates into partially separable Newton
 steps which can then be computed in decentralized fashion, i.e., based only on neighbor-to-neighbor
 communication. Moreover, we show that this numerical framework for decentralized real-time iterations in distributed NMPC
 allows for closed-loop stability guarantees [4] and for scalability [5]. Our findings are illustrated with several examples including multiple 
real-time implementations [6,7].

1] Gros, S., Zanon, M., Quirynen, R., Bemporad, A., & Diehl, M. (2020). From linear to nonlinear MPC:
bridging the gap via the real-time iteration. International Journal of Control, 93(1), 62-80.
[2] Christofides, P. D., Scattolini, R., de la Pena, D. M., & Liu, J. (2013). Distributed model predictive 
control: A tutorial review and future research directions. Computers & Chemical Engineering, 51,
21-41.
[3] Stomberg, G., Engelmann, A., & Faulwasser, T. (2022). Decentralized non-convex optimization via
bi-level SQP and ADMM. In 61st Conference on Decision and Control (CDC), 273-278.
[4] Stomberg, G., Engelmann, A., Diehl, M., & Faulwasser, T. (2024). Decentralized real-time iterations
for distributed nonlinear model predictive control. arXiv preprint arXiv:2401.14898.
[5] Stomberg, G., Raetsch, M., Engelmann, A., & Faulwasser, T. (2025). Large problems are not necessarily hard: A case study on distributed NMPC paying off. European Control Conference
[6] Stomberg, G., Ebel, H., Faulwasser, T., & Eberhard, P. (2023). Cooperative distributed MPC via de-
centralized real-time optimization: Implementation results for robot formations. Control Engineering 
Practice, 138, 105579.
[7] Stomberg, G., Schwan, R., Grillo, A., Jones, C. N., & Faulwasser, T. (2025). Cooperative distributed model predictive control for embedded systems: Experiments with hovercraft formations. 2025 IEEE International Conference on Robotics and Automation

Zoomlink:
https://tuhh.zoom.us/j/81920578609?pwd=TjBmYldRdXVDT1VkamZmc1BOajREZz09

Axons are micrometer-thin cables that transmit signals across the brain. Their size affects how fast signals travel, making axon diameter a key determinant of brain function -- and, when altered, a potential marker of disease. In theory, MRI is sensitive to axon size through the physics of water diffusion, but this sensitivity has remained unproven in real-world settings for decades. In this talk, I'll present recent advances in validating MRI-based axon radius estimates using experimental MRI and high-resolution microscopy of more than 46 million axons across the human brain.

Zoomlink:
https://tuhh.zoom.us/j/87285771127?pwd=bjlWT3AyQncwajZQN0l3dVd1WXJmZz09

In this talk, we review the underappreciated theorem by Lotz that tells us that every strongly continuous operator semigroup on a Grothendieck space with the Dunford-Pettis property is automatically uniformly continuous. A large class of spaces that carry these geometric properties are L^\infty for non-negative measure spaces. This shows once again that $L^\infty$-spaces have to be treated differently.

Atmospheric motion covers a broad range of temporal and spatial scales. Low- and high-pressure systems can influence us for days or even weeks, and they extend up to hundreds of kilometers. In contrast, sound waves pass by in seconds with wavelengths of centimetres to meters. Implicit-explicit (IMEX) time stepping methods can help avoid drastic limitations on the time step induced by this variety of scales without requiring computationally expensive fully nonlinear implicit solves.
We discuss (IMEX-) Spectral Deferred Correction (SDC) methods in the context of Runge-Kutta methods (RKM) and apply SDC to test cases, which are relevant to numerical weather prediction. First, we use RKM theory to:
1. Construct new SDC methods that increase convergence order by 2 per iteration, in contrast to the increase of 1 in general SDC methods;
2. Construct SDC methods that conserve quadratic invariants;
3. Show that SDC can be symmetric but not symplectic for finite iterations.
Second, we apply implicit-explicit (IMEX) SDC to fluid dynamical problems that are relevant to numerical weather prediction. In particular, we compare IMEX-SDC, multistep and RKM time integrators for the Galewsky test case using the Python spectral method framework Dedalus. We demonstrate that SDC methods have superior stability properties and can provide a shorter time to solution for comparable errors. In addition, we outline strategies that could further reduce simulation times by using the SDC residual to minimise the computational effort. Finally, we apply SDC to the compressible Euler equations using compatible finite element methods, demonstrating their applicability to more complex atmospheric models.

Zoomlink:
https://tuhh.zoom.us/j/81920578609?pwd=TjBmYldRdXVDT1VkamZmc1BOajREZz09