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Robust monotone gradient‐based discrete‐time iterative learning control
Author(s) -
Owens D. H.,
Hatonen J. J.,
Daley S.
Publication year - 2009
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.1338
Subject(s) - iterative learning control , robustness (evolution) , monotonic function , monotone polygon , control theory (sociology) , computer science , robust control , inverse , convergence (economics) , mathematical optimization , nonlinear system , mathematics , control (management) , artificial intelligence , mathematical analysis , biochemistry , chemistry , physics , geometry , quantum mechanics , economics , gene , economic growth
This paper considers the use of matrix models and the robustness of a gradient‐based iterative learning control (ILC) algorithm using both fixed learning gains and nonlinear data‐dependent gains derived from parameter optimization. The philosophy of the paper is to ensure monotonic convergence with respect to the mean‐square value of the error time series. The paper provides a complete and rigorous analysis for the systematic use of the well‐known matrix models in ILC. Matrix models provide necessary and sufficient conditions for robust monotonic convergence. They also permit the construction of accurate sufficient frequency domain conditions for robust monotonic convergence on finite time intervals for both causal and non‐causal controller dynamics. The results are compared with recently published results for robust inverse‐model‐based ILC algorithms and it is seen that the algorithm has the potential to improve the robustness to high‐frequency modelling errors, provided that resonances within the plant bandwidth have been suppressed by feedback or series compensation. Copyright © 2008 John Wiley & Sons, Ltd.