Boundary Control

Boundary control focuses on manipulating the boundaries of a system governed by partial differential equations (PDEs) to achieve a desired internal state. Current research emphasizes data-driven approaches, employing neural networks and reinforcement learning algorithms, alongside model-based methods like backstepping and model predictive control (MPC), often utilizing reduced-order models such as Proper Orthogonal Decomposition (POD). These techniques are applied across diverse fields, including fluid dynamics, traffic flow, and robotics, offering faster and potentially more efficient solutions compared to traditional optimization methods. The development of robust and efficient boundary control strategies holds significant promise for optimizing various engineering and scientific systems.