A State‐Space Implementation of Anti‐Causal Iterative Learning Control

Iterative learning control is used to find input signals which match a previously generated output trajectory by repetitively correcting the inputs through the tracking errors. Using causal learning operators this only works up to a relative degree of one. We overcome this restriction with an anti‐causal approach following [1] and show a practical implementation in the state‐space with some examples. We also motivate the method as useful to determine inputs for simulations and test‐rigs. (© 2010 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)