An Integrated Model Predictive Iterative Learning Control Strategy for Batch Processes

A novel integrated model predictive iterative learning control (MPILC) strategy is proposed in this paper. It systematically integrates batch-axis information and time-axis information into one uniform frame, namely the iterative learning controller (ILC) in the domain of batch-axis, while a model predictive controller (MPC) with time-varying prediction horizon in the domain of time-axis. As a result, the operation policy of batch process can be regulated during one batch, which leads to superior tracking performance and better robustness against disturbance and uncertainty. The convergence and tracking performance of the proposed learning control system are firstly given rigorous description and proof. Lastly, the effectiveness of the proposed method is verified by examples.