Time Optimal Control

Time-optimal control aims to find the fastest way to achieve a desired outcome in a dynamic system, a crucial problem across robotics and autonomous systems. Current research focuses on developing efficient algorithms, such as model predictive control (MPC) and reinforcement learning (RL), often incorporating neural networks for improved speed and adaptability, particularly in challenging scenarios with constraints like limited thrust or obstacle avoidance. These advancements are significantly impacting fields like autonomous drone racing and industrial robotics by enabling faster, more agile, and safer operations.