Control Problem

Control problems, aiming to design algorithms that guide systems towards desired states, are a central focus in numerous scientific fields. Current research emphasizes developing stable and efficient control policies, often leveraging neural networks (including neural ODEs and deep residual networks) within reinforcement learning frameworks (like Q-learning and actor-critic methods) or model predictive control approaches. These advancements are driven by the need for robust solutions to complex, continuous-time systems, particularly in applications like robotics, biomedical engineering, and fluid dynamics, where accurate and computationally efficient control is crucial. The development of improved algorithms and theoretical analyses, such as those focusing on stability and convergence rates, is significantly impacting the ability to solve challenging real-world control problems.