The field of control and optimization is moving towards the development of more advanced and sophisticated methods for handling complex systems. Researchers are focusing on creating innovative approaches to address challenges such as uncertainty, nonlinearity, and high dimensionality. One notable trend is the use of surrogate models and uncertainty quantification to improve the accuracy and efficiency of control and optimization algorithms. Another area of research is the development of hybrid systems frameworks that can model and analyze complex systems with both continuous and discrete dynamics. Furthermore, there is a growing interest in applying control theory to real-world problems such as online revenue systems and nonlinear system identification. Noteworthy papers in this area include:

• A paper on multi-stage model predictive control for slug flow crystallizers, which proposes a novel dynamic model and surrogate models with uncertainty quantification capabilities.
• A paper on a time splitting based optimization method for nonlinear moving horizon estimation, which introduces novel computationally efficient algorithms for solving nonlinear MHE problems.