Recursive filtering of networked nonlinear systems: a survey

  • Jingyang Mao
  • , Ying Sun
  • , Xiaojian Yi*
  • , Hongjian Liu
  • , Derui Ding
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

215 Citations (Scopus)

Abstract

Recursive filtering for nonlinear systems, one of the core technologies of modern industrial systems, is an ever-increasing research topic from the control and computer communities. Some challenges from communication scheduling, limited bandwidth as well as security vulnerability have to be seriously handled though the applications of communication technologies bring into some conveniences. As such, it is of utmost significance in theory and great importance in applications to establish engineering-feasible recursive filtering algorithms for networked nonlinear systems. This paper focuses on the development of this topic and provides an up-to-date survey of the existing nonlinear filtering techniques. The introduction of three classes of communication protocols is first presented in great detail, and then comprehensive reviews and summaries of the nonlinear recursive filtering problems with Gaussian/non-Gaussian noises are elaborated according to different strategies responding to nonlinear functions or noises. Particularly, the reviews are layout from the extended Kalman filtering, the unscented/cubature Kalman filtering, the set-membership filtering as well as the (Formula presented.) filtering. Furthermore, several challenging issues are raised to stimulate further related theoretical research and practical applications in this field.

Original languageEnglish
Pages (from-to)1110-1128
Number of pages19
JournalInternational Journal of Systems Science
Volume52
Issue number6
DOIs
Publication statusPublished - 2021

Keywords

  • Nonlinear systems
  • communication protocols
  • cyber-attacks
  • recursive filtering

Fingerprint

Dive into the research topics of 'Recursive filtering of networked nonlinear systems: a survey'. Together they form a unique fingerprint.

Cite this