Data-Driven Feedback ILC Design for Batch Processes via Two-Dimensional System Theory

This study investigates a novel data-driven feedback iterative learning control algorithm under the two-dimensional system framework for batch processes. A set of linear two-dimensional models obtained from plant data by the dynamic linearization technique provides the information required to adjust the parameters of the presented control algorithm. The convergence of the data-driven control approach is derived by a rigorous analysis based on two-dimensional system theory such that the tracking error converges asymptotically along both time-axis and iteration-axis. A simulation example is presented to verify the validity of the researched scheme.