Point-to-point tracking of integrated predictive iterative learning control by using updating-reference and CARIMA model

For point-to-point tracking control problem of batch process, a novel design method based on Controlled Auto-regressive Integrated Moving Average (CARIMA) model and updating-reference is proposed in this paper. In the proposed approach, integrated predictive iterative learning control (IPILC) is used for the trajectory tracking control. Comparing with other point-to-point tracking control algorithms, the proposed control scheme performs better in robustness, and reduces the computation load which occurs in those algorithms based on the lifted model for non-Lyapunov-stable systems. Furthermore, updating-reference relaxes the constraints for system outputs and leads to faster convergence than the fixed-reference control algorithms. Simulation results on typical systems show the effectiveness of the proposed algorithm.