This book provides a comprehensive update and overview of iterative learning control theory and techniques relevant to industrial automation, and focuses on new research directions for the 21st century. Thorough, well-organized, and completely up-to-date, it examines all the important aspects of this emerging technology. Iterative Learning Control: Analysis, Design, Integration and Applications provides dynamic coverage of ILC's history, its expanding real-world applications, and its robustness and convergence. Also included are sampled-data and discrete-time issues, design guidelines and quadratic criterion, the ability of dynamic systems to learn, time-delay problem, integration (with neural network, fuzzy logic and wavelet), direct learning, and identification, in addition to ILC's possible applications to batch and welding processes, neuromuscular stimulation, and other fast-changing fields. The contributions are written by some of the leading internationally recognized researchers in ILC. Iterative Learning Control: Analysis, Design, Integration and Applications will be of interest to researchers and engineers in robotics, automation, systems and control, and signal processing.