Horizon Control

Horizon control, encompassing techniques like receding horizon control (RHC) and model predictive control (MPC), optimizes system behavior over a finite time horizon, repeatedly adjusting actions based on updated information. Current research emphasizes integrating horizon control with safety constraints, often using control barrier functions (CBFs) to guarantee safety while improving performance, and employing reinforcement learning to optimize controller parameters. These advancements are significantly impacting various fields, including robotics (e.g., autonomous navigation, multi-agent coordination), transportation (e.g., eco-driving, delivery optimization), and network communication, by enabling safer, more efficient, and adaptable systems.