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Sparse Data-Driven Stable Kernel Representation Based on Distributed Optimization

  • Shanghai Aerospace Control Technology Institute

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper presents the construction of the sparse data-driven stable kernel representation (S-D2-SKR) based on distributed optimization. The sparse data-driven stable kernel representation is proposed with the formulated optimization problems solved by alternating direction method of multipliers (ADMM). The realization of sparse data-driven stable kernel representation is implemented in three different ways including the lasso-type optimization and the modified optimization based on L1 norm and the structural sparse optimization based on L21 norm. Finally, the effectiveness of the proposed methods is verified on the case studies.

Original languageEnglish
Title of host publication2025 8th International Conference on Robotics, Control and Automation Engineering, RCAE 2025
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages730-735
Number of pages6
ISBN (Electronic)9798350392647
DOIs
StatePublished - 2025
Event2025 8th International Conference on Robotics, Control and Automation Engineering, RCAE 2025 - Xi'an, China
Duration: 24 Oct 202526 Oct 2025

Publication series

Name2025 8th International Conference on Robotics, Control and Automation Engineering, RCAE 2025

Conference

Conference2025 8th International Conference on Robotics, Control and Automation Engineering, RCAE 2025
Country/TerritoryChina
CityXi'an
Period24/10/2526/10/25

Keywords

  • Sparse data-driven stable kernel representation
  • alternating direction method of multipliers
  • distributed optimization

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