The recent developments in the field of control systems are significantly advancing the capabilities and efficiency of process control, particularly through the integration of open-source tools and innovative modeling techniques. There is a notable shift towards leveraging open-source software, such as Julia-based packages, to democratize access to advanced control algorithms, thereby enhancing transparency and reproducibility in research. This trend is complemented by the exploration of neural network-based approaches, particularly within the framework of port-Hamiltonian systems, which offer new ways to design stable and efficient distributed control policies without the need for constraining parameters during optimization. Additionally, data-driven methods are gaining traction for decentralized control design in large-scale systems, reducing the dependency on extensive modeling processes. The field is also witnessing advancements in model predictive control, with a focus on reducing communication and computational costs through multiparametric programming, which is crucial for real-time applications. These innovations collectively push the boundaries of what is achievable in control systems, making them more robust, efficient, and adaptable to complex, real-world scenarios.