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A State‐Space Implementation of Anti‐Causal Iterative Learning Control
Author(s) -
Becker Urs,
Damm Tobias
Publication year - 2010
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.201010292
Subject(s) - iterative learning control , trajectory , state space , computer science , tracking (education) , control (management) , space (punctuation) , state (computer science) , control theory (sociology) , causal model , artificial intelligence , algorithm , mathematics , statistics , physics , psychology , operating system , pedagogy , astronomy
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)