Trajectory Tracking Control

Trajectory tracking control focuses on designing algorithms that enable robots and other autonomous systems to precisely follow pre-planned paths, a crucial aspect for numerous applications. Current research emphasizes robust control strategies that address uncertainties like environmental disturbances, model inaccuracies, and cyberattacks, often employing techniques like model predictive control (MPC), adaptive control, sliding mode control, and reinforcement learning (RL), sometimes incorporating deep neural networks for improved performance. These advancements are vital for enhancing the reliability and performance of autonomous systems in diverse fields, from robotics and aerospace to underwater exploration and industrial automation.