A robust integrated predictive iterative learning control based on updating reference for point-to-point tracking

A novel control technique is proposed by combining P-type Iterative learning control (ILC) and model predictive control (MPC) with updating-reference for point-to-point tracking problem of batch process. In this paper, a batch-to-batch updating-reference, which passes through the desired points, is designed as the tracking trajectory within batch. The update law consists of two parts: P-type ILC and MPC. Based on the updating-reference, MPC can suppress effectively model perturbations and disturbances. Comparing with other point-to- point tracking algorithms, the proposed algorithm performs better in robustness. Furthermore, updating-reference relaxes the output constraints, and it also leads to faster convergence and more extensive range of application than the fixed-reference control algorithms. Simulation results of a numerical case show better performance of the proposed approach.