Approximate non-linear model predictive control with safety-augmented neural networks

A recent study explores how neural networks can enhance model predictive control (MPC) by making it faster and more efficient. This is significant because MPC is crucial for ensuring stability and meeting constraints in complex systems, but it often involves slow computations. By integrating safety measures, the research promises reliable performance even when approximations are made, which could lead to broader applications in various fields, from robotics to autonomous vehicles.