Terminal ILC design and analysis via a dynamical predictive model

Terminal iterative learning control (TILC) has been developed to track a single desired point at the terminal end of operation interval over iterations. In this paper, the feedback control knowledge of previous time instants is utilized via an equivalent dynamical predictive model to update the input signals for the TILC problem. The proposed scheme consists of a control input updating law with feedback information and a parameter updating law together. The new approach is a data-driven control strategy where the controller design and analysis requires only the measurement I/O data without using any model information of the plant. The effectiveness of the proposed approach is guaranteed by rigorous analysis.