This monograph studies the design of robust, monotonically-convergent iterative learning controllers for discrete-time systems. Considering iterative learning control (ILC) in the iteration domain, a unified analysis and design framework is presented that enables designers to consider both robustness and monotonic convergence for typical uncertainty models. Topics include: Conversion of the ILC system, which has dynamics in both the time and iteration domains, into the supervector framework, with dynamics only in the iteration domain.- Development of iteration-domain uncertainty models in the supervector framework.- ILC design for monotonic convergence for plant subject to parametric interval uncertainty in the Markov matrix.- Algebraic H-infinity design for ILC for plant subject to iteration-domain frequency uncertainty.- Kalman-filter-based ILC algorithms for plant subject to iteration-domain stochastic uncertainties.- Analytical determination of the base-line error of ILC algorithms.

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