Control

H-Infinity Control

H-infinity control is a robust control synthesis method that designs controllers by minimizing the H-infinity norm of the closed-loop transfer function from disturbances to regulated outputs, guaranteeing worst-case performance bounds. Introduced by George Zames in 1981, it frames controller design as an optimization problem that explicitly accounts for model uncertainty and disturbance rejection, unlike LQG methods that assume known Gaussian noise.

Why it matters for physical AI

Robots deployed outside labs face payload variation, contact disturbances, and unmodeled dynamics; robust synthesis techniques like H-infinity provide stability guarantees that purely learned controllers typically lack, and inform hybrid learning-plus-control architectures.