Control

Adaptive Control

Adaptive control is a class of control techniques in which controller parameters are adjusted online to cope with unknown or time-varying plant dynamics, such as an unmeasured payload or changing friction. Classical schemes include model reference adaptive control (MRAC) and self-tuning regulators, with stability typically argued via Lyapunov methods. It differs from robust control, which fixes a single controller designed to tolerate bounded uncertainty.

Why it matters for physical AI

Robots in unstructured settings constantly pick up objects of unknown mass and wear over time; adaptive mechanisms, whether classical or implemented as in-context adaptation in learned policies, keep performance from degrading.