Control Co

Control co-design focuses on optimizing the interplay between control systems and their associated communication or design processes, aiming to improve efficiency, performance, and robustness. Current research emphasizes integrating machine learning techniques, such as reinforcement learning and Koopman operators within autoencoder frameworks, to achieve this co-optimization across diverse applications. These approaches are being applied to areas ranging from energy systems and robotic control to communication networks, demonstrating improvements in resource utilization and system performance. The resulting advancements have significant implications for various fields, enabling more efficient and adaptable systems in diverse real-world scenarios.