兰州大学机构库
Deep Bilinear Koopman Model Predictive Control for Nonlinear Dynamical Systems
D. Zhao; B. Li; F. Lu; J. She; S. Yan
2024-05-09
Online publication date2024-05
Source PublicationIEEE Transactions on Industrial Electronics   Impact Factor & Quartile Of Published Year  The Latest Impact Factor & Quartile
ISSN1557-9948
EISSN1557-9948
VolumePPIssue:99Pages:1-10
page numbers10
AbstractThis article presents a deep bilinear Koopman model predictive control (DBKMPC) approach for modelling and control of unknown nonlinear systems. The bilinear model, which has the computational speed of a linear model and the predictive accuracy of a nonlinear model, can accurately characterize a large class of airborne and ground-based robotic systems. Specifically, a bilinear Koopman dynamic deep neural network (BKDDNN) is developed to learn the finite-dimensional bilinear Koopman operator in the lifting space without prior knowledge or system parameters. Moreover, the bilinear model is integrated into the standard model predictive control (MPC) optimization problem, facilitating the solution of the bilinear optimization problem. In such a way, the proposed DBKMPC avoids the problems of excessive inductive bias and selection difficulty of dictionary functions encountered by the existing methods, so that it enables a more effective solution to the problem of modeling and control of nonlinear robotic systems. The experimental results show that the proposed DBKMPC method surpasses the existing representative methods in terms of prediction and control performance.
KeywordBilinear Koopman operator model predictive control (MPC) neural network modeling
PublisherIEEE
DOI10.1109/TIE.2024.3390717
Indexed ByIEEE ; SCIE
Language英语
WOS Research AreaAutomation & Control Systems ; Engineering ; Instruments & Instrumentation
WOS SubjectAutomation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS IDWOS:001218650500001
Original Document TypeArticle ; Early Access
Citation statistics
Document Type期刊论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/588982
Collection兰州大学
信息科学与工程学院
Affiliation
1.School of Information Science and Engineering, Lanzhou University, Lanzhou, China
2.School of Information Science and Engineering, Lanzhou University, Lanzhou, China
3.School of Information Science and Engineering, Lanzhou University, Lanzhou, China
4.School of Engineering, Tokyo University of Technology, Hachioji, Japan
5.School of Information Science and Engineering, Lanzhou University, Lanzhou, China
Recommended Citation
GB/T 7714
D. Zhao,B. Li,F. Lu,et al. Deep Bilinear Koopman Model Predictive Control for Nonlinear Dynamical Systems[J]. IEEE Transactions on Industrial Electronics,2024,PP(99):1-10.
APA D. Zhao,B. Li,F. Lu,J. She,&S. Yan.(2024).Deep Bilinear Koopman Model Predictive Control for Nonlinear Dynamical Systems.IEEE Transactions on Industrial Electronics,PP(99),1-10.
MLA D. Zhao,et al."Deep Bilinear Koopman Model Predictive Control for Nonlinear Dynamical Systems".IEEE Transactions on Industrial Electronics PP.99(2024):1-10.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Altmetrics Score
Google Scholar
Similar articles in Google Scholar
[D. Zhao]'s Articles
[B. Li]'s Articles
[F. Lu]'s Articles
Baidu academic
Similar articles in Baidu academic
[D. Zhao]'s Articles
[B. Li]'s Articles
[F. Lu]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[D. Zhao]'s Articles
[B. Li]'s Articles
[F. Lu]'s Articles
Terms of Use
No data!
Social Bookmark/Share
No comment.
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.