This is the first chapter to consider model-based optimization as a mechanism for creating convergent Iterative Learning Control (ILC) algorithms. More precisely, Gradient-based ILC Algorithms are introduced together with a discussion of implementation and convergence properties. Robustness properties in the presence of model uncertainties are discussed and a rigorous analysis of the difficulties met in applying Gradient-based ILC to non-minimum phase (NMP) systems is characterized in terms of rapid convergence characteristics followed by NMP-induced slow convergence. Extension to point-to-point tracking task is also included and the design process is linked to classical (non-iterative) feedback design methodologies.


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    Titel :

    Gradient-Based Iterative Learning Control


    Weitere Titelangaben:

    Advances in Industrial Control


    Beteiligte:
    Chu, Bing (Autor:in) / Owens, David H. (Autor:in)


    Erscheinungsdatum :

    13.06.2025


    Format / Umfang :

    28 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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