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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.
|Publication date:||26th June 2007|
|Author:||Hyo-Sung Ahn, Kevin L. Moore, YangQuan Chen|
|Publisher:||Springer London Ltd|
|Categories:||Automatic control engineering,|
Hyo-Sung Ahn has research interests in the areas of robust iterative learning control, periodic adaptive learning control, networked control systems, neural networks, mobile robotics, navigation, biomechatronics, and aerospace engineering. He was research engineer in Space Development and Research Center, Korea Aerospace Indusstries LTD, Korea, and Upper Midwest Aerospace Consortium, USA. He received the M.S. degree from the University of North Dakota in Aerospace Engineering and the Ph.D. in Electrical Engineering from Utah State University. Dr. Ahn, with his co-authors, has been the primary developer of the ideas in the monograph and has a deep understanding of the design ...More About Hyo-Sung Ahn, Kevin L. Moore, YangQuan Chen