Path Following Control

Path following control focuses on designing algorithms that enable autonomous systems, such as robots and vehicles, to accurately track a predefined path. Current research emphasizes developing robust and adaptable control policies, often employing model predictive control (MPC), neural networks (including imitation learning), and reinforcement learning techniques to handle complex dynamics and uncertainties. These advancements are crucial for improving the safety and reliability of autonomous systems in various applications, from industrial robotics to self-driving cars, by ensuring precise and predictable movement along desired trajectories. A key challenge remains guaranteeing safety and providing quantitative measures of performance and robustness, particularly for learning-based approaches.