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Iterative Learning Control
Description
Iterative Learning Control takes account of the recently-developed comprehensive approach to robust ILC analysis and design established to handle the situation where the plant model is uncertain. Considering ILC in the iteration domain, 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. Topics include:
• Use of a lifting technique to convert the two-dimensional ILC system, which has dynamics in both the time and iteration domains, into the supervector framework, which yields a one-dimensional system, with dynamics only in the iteration domain.
• Development of iteration-domain uncertainty models in the supervector framework.
• ILC design for monotonic convergence when the plant is subject to parametric interval uncertainty in its Markov matrix.
• An algebraic H-infinity design methodology for ILC design when the plant is subject to iteration-domain frequency uncertainty.
• Development of Kalman-filter-based ILC algorithms when the plant is subject to iteration-domain stochastic uncertainties.
• Analytical determination of the base-line error of ILC algorithms.
• Solutions to three fundamental robust interval computational problems (used as basic tools for designing robust ILC controllers): finding the maximum singular value of an interval matrix, determining the robust stability of interval polynomial matrix, and obtaining the power of an interval matrix.
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