Theoretical foundations
We develop new methods, system architectures and algorithms that process vast amounts of data from the physical world, and use them to determine reliable and effective decisions for regulation and control.
Data science, machine learning and related disciplines allow the IT infrastructure to gather information on the physical world. Building on this knowledge, automation and control provide mechanisms that allow the IT infrastructure to steer machines, devices and entire physical systems in the desired direction. The NCCR Automation closes this control loop by developing methodological principles for controlling complex systems. Our work encompasses:
- Control in a data-rich world As automated systems become more complex, their control is increasingly data-driven. One of our core concerns is deriving the right decisions and meaningful feedback from this wealth of data.
- Control in an uncertain world Malfunctions can occur in any automation system – due to component failures or failures in the communication network, unforeseen external influences or human intervention. We strive to develop robust, resilient control systems that can deal with real-life conditions.
- Distributed hierarchical control and optimisation The larger an automation system becomes, the more the central control units are pushed to their limits. To scale our methods to increasingly larger systems, we develop distributed hierarchical control and optimisation techniques.
