Convergence issue of non-repetitive iterative learning controllers for large-scale systems

Abstract

In this paper, we embed a set of iterative learning controllers into the procedure of the steady-state optimization for a class of large-scale industrial process that consists of a number of Multiple-Input-Multiple-Output subsystems. The controllers are devised to generate a sequence of control inputs to take responsibility of a sequential step functional control signals with distinct scales. The aim of the control design is to consecutively refine the transient performance of the system. By means of Hausdorff-Young inequality of involution integral, the convergence of the updating law is analyzed in the sense of Lebesgue-p norm. Effectiveness of the proposed control scheme is manifested by simulations.