Predictive Controller

Predictive controllers, aiming to optimize future system behavior by anticipating consequences of current actions, are a central focus in control systems research. Current work emphasizes improving computational efficiency and robustness, particularly through the use of linear and nonlinear model predictive control (MPC), enhanced by techniques like Bayesian optimization, ensemble methods (e.g., Dropout MPC), and data-driven approaches such as Koopman operator methods. These advancements are driving progress in diverse applications, including robotics (locomotion, manipulation, and autonomous navigation), aerospace (quadrotor control), and building automation, where improved safety and performance are key benefits.