Online Model Predictive Control

Online Model Predictive Control (MPC) aims to optimize control decisions in real-time by predicting future system behavior, a crucial aspect for dynamic systems like robots and autonomous vehicles. Current research emphasizes integrating data-driven models, such as Gaussian Processes and neural networks, to handle complex system dynamics and improve computational efficiency, often employing techniques like imitation learning or Koopman operator theory for model estimation and control policy learning. This approach enhances the adaptability and performance of MPC in diverse applications, ranging from agile quadrotor flight to contact-rich manipulation and multi-robot coordination, leading to more robust and efficient control systems.