This overview of iterative learning control theory and techniques is relevant to industrial automation, and focuses on research directions for the 21st century. It examines the important aspects of this emerging technology, and provides 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 problems, 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. This volume should be of interest to researchers and engineers in robotics, automation, systems and control, and signal processing.