Design of integrated iterative learning control for batch processes based on online model modification

Considering conventional iterative learning control (ILC) is actually an open-loop control from the view of a separate batch, which is difficult to guarantee the performance of the batch process when uncertainties and disturbances exist, an integrated iterative learning control based on online model modification is proposed in this paper. Online model modification is used to improve the accuracy of neuro-fuzzy model (NFM), and an integrated iterative learning control is used to guarantee the control performances along time and cycle by integrating batch-axis information and time-axis information into one uniform frame. We make the first attempt to give rigorous convergence analysis of the proposed learning control system based on online model modification. An illustrative example is presented to demonstrate the performance of the proposed method.