Control Algorithm

Control algorithms aim to design systems that guide processes or machines to achieve desired behaviors, a crucial aspect across diverse fields from robotics and autonomous vehicles to industrial automation and power systems. Current research emphasizes developing robust and efficient algorithms, focusing on techniques like model predictive control (MPC), reinforcement learning (RL), and PID control, often adapted for specific applications and hardware constraints such as FPGAs. These advancements are driving improvements in areas such as autonomous navigation, precise trajectory tracking, and safe operation in complex or uncertain environments, with significant implications for various industries.