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With Iterative Learning Control Algorithms and Experimental Benchmarking the authors discuss the main methods of iterative learning control (ILC) and its interactions, as well as comparator performance that is so crucial to the end user. They provide an integrated coverage of the major approaches to-date in terms of basic systems theoretic properties, design algorithms, and experimentally measured performance as well the links with repetitive control and other related areas. A large part of the experimental verification comes from a joint research programme co-directed by the authors whose deliverables to-date include the design, commissioning and use of testbed facilities on which ILC and repetitive control algorithms can be experimentally compared. The authors present the results of this research, which has led to the development of the application of ILC in robotic systems for rehabilitation systems for stroke patients. *Provides comprehensive coverage of the main approaches to iterative learning control (ILC) and their relative advantages and disadvantages *Presents the leading research in the field along with a unique experimental benchmarking system *Demonstrates how this approach can extend out from engineering to other areas and, in particular, new research into its use in healthcare systems/ rehabilitation robotics
