Control

Model Predictive Control (MPC)

Model predictive control (MPC) is a feedback control strategy that, at every timestep, solves a finite-horizon optimal control problem using a dynamics model, applies the first action of the optimized sequence, and re-plans from the newly measured state. This receding-horizon scheme handles state and input constraints explicitly and anticipates future dynamics, at the cost of solving an optimization problem online, often at hundreds of hertz.

Why it matters for physical AI

Receding-horizon replanning powers quadruped and humanoid balance, drone racing, and autonomous driving, and it increasingly pairs with learned dynamics models to combine data-driven accuracy with constraint-aware optimization.