The Division of Decision and Control Systems conducts research, education and industry/society interplay within modeling, identification, control, learning and optimisation of dynamical systems. This includes everything from climate-smart energy systems to self-driving cars.
The emerging information and communication infrastructures in the traffic systems give completely new ways of improving the efficiency and safety of transport.
We work on the mathematical foundations of modern learning techniques and algorithms. More precisely, we currently develop tools for dimensionality reduction and clustering, neural networks, and reinforcement learning.
Research on Networked control and robotics spans a wide variety of related topics with theoretical as well as practical relevance. On the networked control side, research involves control and task planning of multi-agent systems under sensing and communication constraints.
The division has a broad research activity in the general area of control and optimisation. We are developing fundamental theory for dynamical systems and are constantly attempting to push the boundaries for advanced control strategies such as networked, hybrid and asynchronous control.
Dynamical process models underpin advanced process control and obtaining this type of model is typically very costly and time consuming for the complex processes in question.
Control engineering has traditionally developed theory and practice where certain properties of the cyber and physical world are assumed to have negligible impact on the resulting control performance.
System Identification, or the discipline of learning dynamical systems, is an area closely related to cyber-physical systems, as well as real-time big data analytics, and it provides backbone algorithms for digitalisation of industry and society. Among others, it is core technology in autonomous systems with applications such as smart buildings, self-driving vehicles, and self-learning robots.
