Control and systems theory has an incredible set of tools and methods to understand and ultimately control systems. One of the challenges is, however, as we move to apply these systematic methods to systems outside of engineering (e.g., beyond trains, cars, robotics), many of the methods become intractable on the large scale systems we observe in, for example, biochemical protein interactions, social organizations and populations, neuron interconnections, and economic markets. Because of the sheer size and uncertainty about these systems, researchers often create network models to represent the architecture that comprises their interactions. We have begun work in leveraging existing and adapting new tools that are better suited to studying such complex networks, because they scale more gracefully. In particular, we are interested in knowing how the structure of these networks impacts their control-related properties. Below are two videos regarding some of our work on this topic. The first is meant to engage the broader public and the second is meant to allow researchers in diverse areas to connect with our work.
Working at the novel interface between control theory and network science raises some very interesting new questions in the theory of control. This an active area of research for us.
Additional implementation details regarding the datasets and software used in this study can be found on this page.