One of the key focal points of our research is the integration of control-theoretic principles, where we are actively working to address issues related to disturbances and the intricate structure of CPS. This is complemented by our exploration of model-based and data-driven techniques, which allow us to harness the power of data to enhance the reliability and robustness of CPS.
Uncertainty is an ever-present challenge in CPS, and we are dedicated to developing solutions that can thrive in the face of this uncertainty, even when prior knowledge is incomplete or missing. The incorporation of stochasticity into our methods further broadens the scope of our research, enabling us to design CPS that can adapt and respond effectively to inherently probabilistic environments.