Nonlinear Model Predictive Control

Nonlinear Model Predictive Control (NMPC) is an advanced control technique aiming to optimize the control inputs of nonlinear dynamical systems over a finite prediction horizon, subject to constraints. Current research emphasizes improving computational efficiency through parallel computing, innovative optimization algorithms (like interior point methods and conjugate gradient), and reduced-order modeling techniques, often incorporating machine learning for model reduction or value function approximation. NMPC's ability to handle complex dynamics and constraints makes it crucial for applications ranging from autonomous navigation (e.g., UAVs, robots, and autonomous vehicles) to advanced robotics and process control, driving significant advancements in these fields.