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This book presents a comprehensive and detailed study on iterative learning control (ILC) for systems with iterationvarying trial lengths. Instead of traditional ILC, which requires systems to repeat on a fixed time interval, this book focuses on a more practical case where the trial length might randomly vary from iteration to iteration. The iterationvarying trial lengths may be different from the desired trial length, which can cause redundancy or dropouts of control information in ILC, making ILC design a challenging problem. The book focuses on the synthesis and analysis of ILC for both linear and nonlinear systems with iterationvarying trial lengths, and proposes various novel techniques to deal with the precise tracking problem under nonrepeatable trial lengths, such as moving window, switching system, and searchingbased moving average operator. It not only discusses recent advances in ILC for systems with iterationvarying trial lengths, but also includes numerousintuitive figures to allow readers to develop an indepth understanding of the intrinsic relationship between the incomplete information environment and the essential tracking performance. This book is intended for academic scholars and engineers who are interested in learning about control, datadriven control, networked control systems, and related fields. It is also a useful resource for graduate students in the above field.
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This product may contain chemicals known to the State of California to cause cancer,
birth defects, or other reproductive harm.
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