The textbook "Iterative Learning Control" provides a comprehensive examination of the design of robust, monotonically converging iterative learning controllers for discrete-time systems. Iterative Learning Control (ILC) has established itself as an effective method that enables significant performance improvements for systems operating in iterative or repetitive processes, such as robotic arms in manufacturing or batch processes in industrial environments. While the fundamentals of ILC design have been well addressed in the literature, two central issues remain of ongoing interest. First, many ILC design strategies assume nominal knowledge of the system to be controlled, whereas a comprehensive approach to robust ILC analysis and design has only recently been developed to address uncertainties in the plant model. Second, it is known that many ILC algorithms do not produce monotonic convergence, which can be crucial in applications. This book offers a unified analysis and design framework for robust, monotonically converging ILC and considers ILC design in the iteration domain rather than the time domain.