Periodic Trajectory

Periodic trajectory research focuses on generating and controlling repetitive movements in various systems, from robots and drones to complex physical processes. Current efforts concentrate on developing robust control algorithms, such as model predictive control and iterative learning control, often incorporating advanced techniques like neural networks and Gaussian processes to handle nonlinear dynamics and uncertainties. These advancements are crucial for improving the precision and reliability of autonomous systems in diverse applications, including robotics, aerospace engineering, and even traffic flow prediction. The ultimate goal is to achieve accurate and efficient trajectory tracking, even in the presence of constraints and unmodeled dynamics.