Model predictive control

Summary

Model predictive control is a feedback strategy that solves a finite-horizon optimal control problem at every sampling time. From the measured state it predicts the future with a model and optimises the controls over a horizon of $N$ steps subject to constraints. It applies only the first control and repeats at the next step. This receding-horizon principle turns open-loop optimisation into a closed-loop controller that handles constraints on states and inputs directly. It is standard in process industry, power electronics and autonomous driving.