Control

Nonlinear Model Predictive Control (NMPC)

Nonlinear model predictive control (NMPC) is a control strategy that repeatedly solves a finite-horizon optimal control problem with nonlinear dynamics and constraints, applies the first action, and re-plans at the next step. Solvers based on sequential quadratic programming or differential dynamic programming, and toolchains like acados, enable high-rate NMPC. It is standard in legged locomotion, agile drone flight, and mobile manipulation.

Why it matters for physical AI

Dynamic robots operating near actuation and contact limits benefit from controllers that reason about constraints explicitly, and NMPC often serves as the low-level backbone beneath learned high-level policies.