Nonlinear MPC

Nonlinear Model Predictive Control (NMPC) optimizes control actions over a prediction horizon to achieve desired system behavior while satisfying constraints, addressing the challenges posed by nonlinear system dynamics. Current research emphasizes efficient computation through parallel algorithms, leveraging sparse structures and techniques like sequential quadratic programming, as well as integrating NMPC with machine learning models (e.g., neural networks, transformers) for improved prediction and control performance in complex scenarios. These advancements are significantly impacting robotics, autonomous driving, and other fields requiring real-time control of complex systems by enabling safer, more robust, and efficient control strategies.