Recursive filtering of networked nonlinear systems: a survey

Jingyang Mao, Ying Sun, Xiaojian Yi, Hongjian Liu and Derui Ding

International Journal of Systems Science, 2021, vol. 52, issue 6, 1110-1128

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 $ H_\infty $ H∞ filtering. Furthermore, several challenging issues are raised to stimulate further related theoretical research and practical applications in this field.

Date: 2021
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Citations: View citations in EconPapers (6)

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DOI: 10.1080/00207721.2020.1868615

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