Differential Dynamic Programming

Differential Dynamic Programming (DDP) is an efficient optimization algorithm used to solve nonlinear optimal control problems, primarily aiming to find optimal control sequences for dynamic systems. Current research focuses on extending DDP's capabilities to handle complex scenarios, including hybrid systems, constraints (equality and inequality), and uncertainties, often employing multi-shooting methods and augmented Lagrangian approaches to improve robustness and convergence. These advancements are significantly impacting robotics, enabling more agile and safe robot control through applications like model predictive control and trajectory optimization, as well as improving performance in other fields such as inverse reinforcement learning and system identification.