Control

Iterative Learning Control

Iterative learning control is a control technique for systems that repeat the same task, updating the feedforward command after each trial based on the error recorded in previous trials. Over successive repetitions the tracking error converges toward zero, compensating for repeatable disturbances and model errors without requiring an accurate plant model.

Why it matters for physical AI

Repetitive industrial operations such as dispensing, machining, and pick cycles can approach near-perfect tracking through trial-to-trial refinement, a classical complement to learning-based control that improves with every execution.