Inverse model-based and feedback-assisted iterative learning control for a class of batch processes

This paper presents a inverse model-based and feedback-assisted iterative learning control (ILC) for a class of batch processes. The dynamics of the processes can be represented by the first-order plus dead time (FOPDT) model. The ILC algorithm is derived based on the inverse model. The robustness of the proposed strategy for the batch processes in the presence of uncertainties in modeling is analyzed. Sufficient conditions guaranteeing convergence of tracking error are stated and proven. Simulation shows that the ILC strategy can improve the process performance gradually as a batch process repeated even there are model mismatches and disturbances.