Hybrid MPC

Hybrid Model Predictive Control (MPC) optimizes control strategies involving both continuous and discrete variables, addressing the challenges of real-time control in complex systems like robots and autonomous vehicles. Current research emphasizes improving computational efficiency through techniques like Generalized Benders Decomposition and sampling-based methods, as well as integrating MPC with other control frameworks such as control barrier functions and reinforcement learning for enhanced safety and robustness. These advancements are significantly impacting various fields, enabling more agile and reliable control in applications ranging from legged robotics and aerial manipulation to autonomous driving and unmanned surface vehicles.