A just-in-time learning based integrated IMC-ILC control strategy for batch processes

This paper deals with issue of two dimensional (2D) control for batch processes. Firstly, in order to derive high efficiency and accuracy process model, a novelty hierarchical searching mechanism just-in-time learning (JITL) model is constructed. Then, on the basis of the JITL model, an integrated internal model control (IMC) and iterative learning control (ILC) 2D control scheme is presented, where the IMC in time-axis can reject disturbances and uncertainties, and the ILC in batch-axis can guarantee the control system asymptotically convergence. Moreover, the initial batch control input trajectory of the IMC-ILC algorithm can be obtained by using the JITL from inverse model system. As a result, not only the issue of model-plant mismatches and real-time disturbances can be solved, but also obtain faster system convergence rate and smaller tracking errors. Finally, a typical batch process is proved to demonstrate the feasibility and superiority.