Improved point-to-point iterative learning control for batch processes with unknown batch-varying initial state

Abstract

In this article, the authors study and solve the problem of point-to-point iterative learning control (P2PILC), which only tracks certain key points with unknown batch-varying initial state, thereby eliminating the impact on the output of the initial state error at the final tracking time instants. By completely considering the degree of freedom in point-to-point (P2P) control, an update learning law is designed to compensate the initial state error, and the convergence of the error at the tracking points is proved. At last, the effectiveness of the proposed method is validated by simulation.