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MORE ABOUT THIS BOOK
Main description:
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.
Contents:
Iterative Learning Control Overview.- An Overview of the ILC Literature.- The Super-vector Approach.- Robust Interval Iterative Learning Control.- Robust Interval Iterative Learning Control: Analysis.- Schur Stability Radius of Interval Iterative Learning Control.- Iterative Learning Control Design Based on Interval Model Conversion.- Iteration-domain Robustness.- Robust Iterative Learning Control: H? Approach.- Robust Iterative Learning Control: Stochastic Approaches.- Conclusions.
PRODUCT DETAILS
Publisher: Springer (Springer London Ltd)
Publication date: October, 2010
Pages: 230
Weight: 385g
Availability: Available
Subcategories: Biomedical Engineering
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