Optimal Control

Optimal control aims to find the best way to manipulate a system's inputs to achieve a desired outcome, often by minimizing a cost function subject to constraints. Current research emphasizes efficient algorithms for solving optimal control problems, particularly for high-dimensional systems, with a focus on methods like model predictive control, reinforcement learning (including deep reinforcement learning and its variants), and deep operator networks. These advancements are driving progress in diverse fields, including robotics (trajectory optimization, safe navigation, and control of complex systems), and process control (e.g., optimizing energy consumption and ensuring safety).