Control

Predictive Functional Control

Predictive functional control (PFC) is a simplified form of model predictive control, developed by Jacques Richalet, that parameterizes the future control input as a weighted sum of simple basis functions and enforces reference tracking only at a few coincidence points on the prediction horizon. The resulting computation is light enough for fast industrial loops, which made PFC one of the earliest widely fielded predictive control techniques.

Why it matters for physical AI

PFC illustrates a recurring deployment lesson: aggressive structural simplification can retain most of predictive control's benefit at a fraction of the compute, an approach echoed in today's real-time MPC for legged robots.