The recent developments in the field of control system design and analysis have seen significant advancements in integrating model identification with controller synthesis to ensure robust and stable control. A notable trend is the incorporation of control-oriented regularization during the identification process, which guarantees the existence of a suitable controller that can enforce robust constraints. This approach, often implemented through quasi Linear Parameter-Varying (qLPV) models, leverages novel scheduling function parameterizations and polytope geometry to enhance the tractability of the learning problem. Additionally, there is a growing focus on certified training frameworks, such as CT-BaB, which utilize branch-and-bound techniques at training time to produce verification-friendly models, significantly improving both the efficiency and the size of the region-of-attraction during verification. Furthermore, advancements in Scaled Relative Graphs (SRGs) have led to a generalized circle criterion, broadening the applicability of frequency-domain analysis to a wider class of nonlinear systems, as demonstrated in the analysis of complex dynamical systems like the controlled Duffing oscillator.

Noteworthy Papers:

• The introduction of control-oriented regularization in model identification ensures robust control constraints, advancing the integration of system identification and controller synthesis.
• The CT-BaB framework for certified training of Lyapunov-stable neural controllers significantly enhances verification efficiency and region-of-attraction size.
• The generalized circle criterion through SRG analysis extends the applicability of frequency-domain methods to broader classes of nonlinear systems.