Control

Robust Control

Robust control is the branch of control theory that designs controllers guaranteeing stability and performance for every plant within a bounded uncertainty set, rather than for one nominal model. Core tools include H-infinity synthesis, mu-analysis for structured uncertainty, and sliding mode control's invariance to matched disturbances. It contrasts with adaptive control, which estimates uncertain parameters online instead of guarding against their worst case.

Why it matters for physical AI

Payload changes, joint friction drift, and contact variability mean robot models are always wrong to some degree, and robust design principles inform everything from joint servos to certifying learned controllers under disturbance bounds.