Advances in iterative learning control: A recent five-year literature review

Iterative learning control (ILC) is a control strategy specifically devised for finite-length batch processes that can be repeatedly executed. By iteratively refining the input signal across successive system trials, ILC enables accurate tracking of a predefined reference trajectory. Since its inception, this control methodology has evolved over four decades into a relatively mature and comprehensive theoretical framework. Nevertheless, in the past decade, the field has lacked systematic review and in-depth discussion on the overall progress of the field, with only a handful of studies offering limited retrospectives within specific subdomains. To provide a holistic understanding of the current state of the art and to identify promising directions for future investigation, this paper presents a literature review of recent key developments from five essential dimensions: system dynamics and settings, signal acquisition and transmission, reference trajectory, algorithm design and analysis, and implementations and applications. For each dimension, we summarize the major advancements and representative contributions, followed by critical discussions and forward-looking perspectives. This review aims to help researchers and practitioners in grasping the prevailing research trends and to inspire further theoretical and applied developments in ILC.