This is an overview of iterative learning control. It can be used as a text or reference for a course at graduate level and is also suitable for self-study and for industry-oriented courses of continuing education. Ranging from aerodynamic curve identification robotics to functional neuromuscular stimulation, Iterative Learning Control (ILC), which started in the early 1980s, is found to have wide applications in practice. Generally, a system under control may have uncertainties in its dynamic model and its environment. One attractive point in ILC lies in the utilization of the system repetitiveness to reduce such uncertainties and in turn to improve the control performance by operating the system repeatedly. This mograph emphasises both theoretical and practical aspects of ILC, and provides some developments in ILC convergence and robustness analysis. The book also considers issues in ILC design. Several practical applications are presented to illustrate the effectiveness of ILC. The applied examples provided in this mograph are particularly beneficial to readers who wish to capitalise the system repetitiveness to improve system control performance.