Iterative Learning Control Algorithms and Experimental Benchmarking by Eric Rogers, Bing Chu, Christopher Freeman, Paul Lewin
Requirements: .PDF reader, 21 mb
Overview: The book discusses the main methods of iterative learning control (ILC) and its interactions, as well as comparator performance that is so crucial to the end user. The book provides integrated coverage of the major approaches to-date in terms of basic systems, theoretic properties, design algorithms, and experimentally measured performance, as well as the links with repetitive control and other related areas.
Genre: Non-Fiction > Engineering
Key features:
• Provides comprehensive coverage of the main approaches to ILC and their relative advantages and disadvantages.
• Presents the leading research in the field along with experimental benchmarking results.
• 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.
The book is essential reading for researchers and graduate students in iterative learning control, repetitive control and, more generally, control systems theory and its applications.
Download Instructions:
Iterative_Learning_Control_Algorithms_and_Experimental_Benchmarking.pdf
Iterative_Learning_Control_Algorithms_and_Experimental_Benchmarking.pdf
