Zuse Research Colloquium

In this talk model predictive control (MPC) is utilized to stabilize a class of linear time-varying parabolic partial differential equations (PDEs). In our first example the control input is only finite-dimensional, i.e., it enters as a time-depending linear combination of finitely many indicator functions whose total supports cover only a small part of the spatial domain. In the second example the PDE involve switching coefficient functions. We discuss stabilizability and the application of reduced-order models to derive algorithms with closed-loop guarantees.

This is joint work with Behzad Azmi (Konstanz), Michael Kartmann (Konstanz), Mathia Menucci (Stuttgart), Jan Rohleff (Konstanz), and Benjamin Unger (Stuttgart).

Many real-world systems are composed of agents whose interactions result in a collective swarm behavior that may be complex, unexpected, and/or unintended. We highlight intriguing cases of interplay between the micro-scale behavior of agents and the macro-scale performance of the swarm, with a particular emphasis on heterogeneous systems composed of different types of agents, such as: traffic flow (the role of automation/connectivity on the energy footprint of urban traffic flow), mixed human/robotic groups (transportation of supplies to a disaster area), and biological systems (schools of fish and colonies of penguins). We particularly show how behavior interpretable as ‘equitable’ or ‘altruistic’ is possible to arise from pure survival-of-the-fittest objective functions