Learning-based Control

There is many complex cyber-physical systems where the precise model description can not be derived analytically and therefore one cannot use model-based techniques in these situations. In such cases, due to advances in sensor and processing technologies, one can take advantage of data-driven approaches from machine learning to address these problems. Here, we aim to provide probabilistic guarantees that the system trajectories of unknown dynamical systems satisfy complex specifications.