西安交通大学教师主页管理系统阮小娥 Convergence Analysis in Sense of Lebesgue-p Norm of Decentralized Npn-repetitive Iterative Learning Control for Linear Large-scale Systems Home

Convergence Analysis in Sense of Lebesgue-p Norm of Decentralized Npn-repetitive Iterative Learning Control for Linear Large-scale Systems

Release Time:2025-04-30

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Title of Paper:: Convergence Analysis in Sense of Lebesgue-p Norm of Decentralized Npn-repetitive Iterative Learning Control for Linear Large-scale Systems

Summary:: In this paper, a decentralized iterative learning control strategy is embedded into the
procedure of hierarchical steady-state optimization for a class of linear large-scale industrial processes
which consists of a number of subsystems. The task of the learning controller for each subsystem is to
iteratively generate a sequence of upgraded control inputs to take responsibilities of a sequential step
functional control signals with distinct scales which are determined by the local decision-making units in
the two-layer hierarchical steady-state optimization processing. The objective of the designated strategy
is to consecutively improve the transient performance of the system. By means of the generalized Young
inequality of convolution integral, the convergence of the learning algorithm is analyzed in the sense of
Lebesgue-p norm. It is shown that the inherent feature of system such as the multi-dimensionality and
the interaction may in°uence the convergence of the non-repetitive learning rule. Numerical simulations
illustrate the e®ectiveness of the proposed control scheme and the validity of the conclusion.