Abstract
As advanced control strategies, both iterative learning control (ILC) and model predictive control (MPC) are widely used in industrial process. Because ILC cannot eliminate the non-repetitive disturbances, ILC and MPC are integrated as model predictive iterative learning control (MPILC) to improve the capability of rejecting disturbances. Although the typical MPILC has a good tracking performance, there is also left some aspects to be developed. Based on a fuzzy model, a modified nonlinear model predictive iterative learning control (NMPILC) is proposed to achieve a better tracking performance and speed up the learning rate. The performance of the modified NMPILC is illustrated by a PH neutralization process.