Dual-Mode Data-Driven Iterative Learning Control: Applications in Precision Manufacturing and Intelligent Transportation Systems

Iterative Learning Control (ILC) provides an effective framework for optimizing repetitive tasks, making it particularly suitable for high-precision applications in both precision manufacturing and intelligent transportation systems (ITS). This paper presents a systematic review of ILC’s developmental progress, current methodologies, and practical implementations across these two critical domains. The review first analyzes the key technical challenges encountered when integrating ILC into precision manufacturing workflows. Through case studies, it evaluates demonstrated improvements in positioning accuracy, surface finish quality, and production throughput. Furthermore, the study examines ILC’s applications in ITS, with particular focus on vehicular motion control applications including autonomous vehicle trajectory tracking, platoon coordination, and traffic signal timing optimization, where its data-driven characteristics enhance adaptability to dynamic environments. Finally, the paper proposes targeted future research directions that are essential for fully realizing ILC’s potential in advancing these interconnected yet distinct fields.