Considering the two-dimension (2D) characteristic and the unknown optimal trajectory problem of the batch processes, an integrated model predictive control-iterative learning control (MPC-ILC) for batch processes is proposed in this paper. Firstly, the batch-axis information and time-axis information are combined into one quadratic performance index. It implies the integration of ILC and MPC algorithm idea, which leads to superior tracking performance and better robustness against disturbance and uncertainty. To address the problem of the unknown optimal trajectory, both time-varying prediction horizon and end product quality control are employed. Moreover, an integrated 2D just-in-time learning (JITL) model is used to improve the predictive accuracy. Furthermore, rigorous description and proof are presented to prove the convergence and tracking performance of the proposedMPC-ILC strategy. The simulation results showthe effectiveness of the proposed method. (C) 2018 The Chemical Industry and Engineering Society of China, and Chemical Industry Press. All rights reserved.