非线性分数扩散方程边界跟踪问题的迭代学习算法

IF 1.2 4区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Networks and Heterogeneous Media Pub Date : 2023-01-01 DOI:10.3934/nhm.2023059
Jungang Wang, Qingyang Si, Jun Bao, Qian Wang
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引用次数: 0

摘要

本文将迭代学习控制技术推广到由非线性分数扩散方程控制的分布参数系统。基于$ P $型和$ PI^{\theta} $型迭代学习控制方法,给出了系统收敛的充分条件。最后,通过数值算例说明了所提迭代格式的有效性。数值结果表明,闭环迭代学习控制方案收敛速度快于开环迭代学习控制方案,$ PI^{\theta} $型迭代学习控制方案收敛速度快于$ P $型和$ PI $型迭代学习控制方案。
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Iterative learning algorithms for boundary tracing problems of nonlinear fractional diffusion equations
In this paper, the iterative learning control technique is extended to distributed parameter systems governed by nonlinear fractional diffusion equations. Based on $ P $-type and $ PI^{\theta} $-type iterative learning control methods, sufficient conditions for the convergences of systems are given. Finally, numerical examples are presented to illustrate the efficiency of the proposed iterative schemes. The numerical results show that the closed-loop iterative learning control scheme converges faster than the open-loop iterative learning control scheme and the $ PI^{\theta} $-type iterative learning control scheme converges faster than the $ P $-type and the $ PI $-type iterative learning control scheme.
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来源期刊
Networks and Heterogeneous Media
Networks and Heterogeneous Media 数学-数学跨学科应用
CiteScore
1.80
自引率
0.00%
发文量
32
审稿时长
6-12 weeks
期刊介绍: NHM offers a strong combination of three features: Interdisciplinary character, specific focus, and deep mathematical content. Also, the journal aims to create a link between the discrete and the continuous communities, which distinguishes it from other journals with strong PDE orientation. NHM publishes original contributions of high quality in networks, heterogeneous media and related fields. NHM is thus devoted to research work on complex media arising in mathematical, physical, engineering, socio-economical and bio-medical problems.
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