The field of nonlinear control and structural health monitoring is witnessing significant developments, with a focus on robust and offset-free tracking of nonlinear systems. Researchers are exploring innovative approaches, including kernelized data-driven predictive control and velocity form formulations for recurrent neural networks, to improve the accuracy and efficiency of control systems. Additionally, there is a growing emphasis on quantifying uncertainty and identifiability in Bayesian inverse problems, particularly in the context of structural health monitoring. This has led to the development of frameworks that rigorously quantify the limits of resolution and uncertainty of inferred states, enabling more informed decision-making. Notable papers in this area include:

• A novel Kernelized Data-Driven Predictive Control scheme that achieves robust and offset-free tracking of nonlinear systems.
• A Bayesian inverse framework that quantifies the limits of resolution and uncertainty in structural health monitoring, providing a rigorous basis for optimizing sensor placement and interpreting diagnostics.