文献类型：期刊文献

英文题名：Modified Newton integration algorithm with noise suppression for online dynamic nonlinear optimization

作者：Huang, Haoen[1];Fu, Dongyang[1];Wang, Guancheng[2];Jin, Long[3,4,5];Liao, Shan[6];Wang, Huan[1]

机构：[1]Guangdong Ocean Univ GDOU, Sch Elect & Informat Engn, Zhanjiang 524025, Peoples R China;[2]Univ Macau, Fac Sci & Technol, Dept Comp & Informat Sci, Macau 999078, Peoples R China;[3]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[4]Guangdong Ocean Univ, Shenzhen Inst, Shenzhen 518108, Peoples R China;[5]Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China;[6]Sichuan Univ, Sch Cybersecur, Chengdu 610065, Peoples R China

年份：2021

卷号：87

期号：2

起止页码：575

外文期刊名：NUMERICAL ALGORITHMS

收录：SCI-EXPANDED(收录号：WOS:000568000500002)、、Scopus(收录号：2-s2.0-85090791009)、WOS

基金：This work is supported by the Fund of Southern Marine Science and Engineering Guangdong Laboratory of Zhanjiang, China under Grant ZJW-2019-08, by the Key Projects of the Guangdong Education Department under Grant 2019KZDXM019, in part by the High-Level Marine Discipline Team Project of Guangdong Ocean University under Grant 002026002009, by the Guangdong Graduate Academic Forum Project under Grant 230420003, by the "First Class" Discipline Construction Platform Project in 2019 of Guangdong Ocean University under Grant 231419026, by the Innovation and Strength Project in Guangdong Province, China (Natural Science) under Grant 230419065, by the Key Lab of Digital Signal and Image Processing of Guangdong Province, China under Grant 2019GDDSIPL-01, by the Industry-University-Research Cooperation Education Project of Ministry of Education under Grant 201801328005, by the Guangdong Graduate Education Innovation Project, Graduate Summer School under Grant 2020SQXX19, by the Guangdong Graduate Education Innovation Project, Graduate Academic Forum under Grant 2020XSLT27, by the Doctoral Initiating Project of Guangdong Ocean University under Grant E13428, and also by the Special Project in Key Fields of Universities in Department of Education of Guangdong Province, China under Grant 2019033.

语种：英文

外文关键词：Online dynamic nonlinear optimization (ODNO); Modified Newton integration (MNI) algorithm; Noise-suppressing; Steady-state error

外文摘要：The solution of nonlinear optimization is usually encountered in many fields of scientific researches and engineering applications, which spawns a large number of corresponding algorithms to cope with it. Besides, with developments of modern cybernetics technology, it imperatively requires some advanced numerical algorithms to solve online dynamic nonlinear optimization (ODNO). Nevertheless, the major existing algorithms are limited to the static nonlinear optimization models, few works considering the dynamic ones, let alone tolerating noise. For the abovementioned reasons, this paper proposes a modified Newton integration (MNI) algorithm for ODNO with strong robustness and high-accuracy computing solution, which can effectively suppress the influence caused by noise components. In addition, the correlative theoretical analyses and mathematical proofs on convergence and robustness of the MNI algorithm are carried out, which indicates that computing solutions of the proposed MNI algorithm can globally converge to relative small value in the presence of various noise or zero noise conditions. Finally, to illustrate the advantages and feasibilities of the proposed MNI algorithm for ODNO problems, four numerical simulation examples and an application to robot manipulator motion generation are performed.