An Anticipatory Terminal Iterative Learning Control Approach with Applications to Constrained Batch Processes

This work presents an anticipatory terminal iterative learning control scheme for a class of batch processes, where only the final system output is measurable and the control input is constant in each operations. The proposed approach works well with input constraints provided that the desired control input with respect to the desired trajectory is within the saturation bound. The tracking error convergence is established with rigorous mathematical analysis. Simulation results are provided to show the effectiveness of the proposed approach.