Frequency-Domain-Based Forgetting-Factor Data-Driven Iterative Learning Control Algorithm

This paper introduces a data-driven iterative learning control (ILC) algorithm based on the forgetting factor within the frequency domain. Different from the conventional time-domain design of the forgetting factor algorithm, this approach employs an adaptive decomposition technique to construct the data model, utilizing frequency-domain sampling data of the system's input and output. Building upon this foundation, the tracking errors undergo orthogonalization to extract maximum error energy, thereby enhancing the convergence speed of the ILC.