What I do

My work focuses on systems and control theory with an emphasis on systems that are subject to significant uncertainty. In particular, I investigate the following topics.

Data-driven modeling and control

• From data and prior knowledge to models and controllers: In this line of research, we focus on system identification/analysis and control synthesis using data and prior knowledge. We extend the notion of data informativity to one that takes general prior knowledge of the system into account. See our recent work, “Data-driven stabilization using prior knowledge on stabilizability and controllability.”

Experiment design

• From prior knowledge to informative data: In this line of research, we focus on finding inputs that force the unknown true system to generate informative data. For this goal, we leverage prior knowledge of the true system. See our recent work, “Experiment design using prior knowledge on controllability and stabilizability.”

Robust control

• From prior knowledge to feedback laws: Here, we investigate fundamental limitations and possibilities of robust feedback laws. What if we go beyond feedback laws that are linear and static? Can we simultaneously stabilize all systems within an arbitrarily large set using time-invariant feedback laws that are nonlinear and/or dynamic? If memory is required, what is the shortest one? For more details, see our recent work “Robust stabilization of linear systems requires nonlinear dynamic feedback.”