This monograph studies the design of robust,
monotonically-convergent iterative learning controllers for
discrete-time systems. It presents a unified analysis and design
framework that enables designers to consider both robustness and
monotonic convergence for typical uncertainty models, including
parametric interval uncertainties, iteration-domain frequency
uncertainty, and iteration-domain stochastic uncertainty. The book
shows how to use robust iterative learning control in the face of
model uncertainty.
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