Convergence issue of nonrrpetitive iterative learning controllers for large-scale systems
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发布时间：2025-04-30
发布时间：2025-04-30
论文名称：Convergence issue of nonrrpetitive iterative learning controllers for large-scale systems
发表刊物：连续离散与脉冲系统动力学A辑
摘要：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.
合写作者：Xiaoe Ruan, Fengmin Chen, Zeungnam Bien
页面范围：771-776
是否译文：否
发表时间：2006-07-24