The theoretical research in the lab has strong focus on control approach development for the full cycle starting from system modelling to the end application. The particular topics include advanced techniques of data analysis and classification along with model uncertainty assessment and fault diagnosis. Special attention is paid to complex and hierarchical systems, where consistency between supervising control and subroutines for different system layers is of particular interest. Concrete control methods in turn include model predictive control, reinforcement learning, adaptive dynamic programming and other optimal control methods, with a special focus on learning aspects. Analysis of robustness properties of control methods in presence of uncertainties is a part of virtually every new control method developed in the lab. Particular attention is paid to the computational uncertainty related to the imperfections of controller implementation in digital and analog devices. For this sake, the lab works on a variety of mathematical methods which explicitly incorporate the said uncertainty and subsequently aim at developing a basis of formally correct and automated controller extraction.