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Convergence and Robustness of Iterative Learning Control For Strongly Positive Systems
Author(s) -
Andres Daniel,
Pandit Madhukar
Publication year - 2002
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1111/j.1934-6093.2002.tb00327.x
Subject(s) - robustness (evolution) , hilbert space , iterative learning control , square integrable function , mathematics , control theory (sociology) , invariant (physics) , linear system , lti system theory , discrete time and continuous time , computer science , pure mathematics , mathematical analysis , control (management) , artificial intelligence , biochemistry , chemistry , mathematical physics , gene , statistics
In this paper, we consider the convergence and robustness of a general iterative learning control scheme for a class of systems which we term “strongly positive”. The analysis is made in the framework of Hilbert‐space theory. Thus the results are valid for discrete‐time as well as continuous‐time systems which may be time‐variant or time‐invariant. For the special case of continuous linear time‐invariant systems which are defined over the Hilbert‐space of square integrable functions, we will give a characterization of strongly positive systems in the frequency domain.