Sliding Mode Cooperative Guidance Law Based on RBF Neural Network Gain Regulation

X. Y. Shi, H. B. Deng*

*Corresponding author for this work

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

Abstract

Aiming at the problems of high-speed jitter of line of sight inclination rate and difficult to meet the dynamic characteristics of actuator in the face of high maneuvering targets in the traditional sliding mode guidance law, a guidance law which can adaptively adjust the benefits of variable structure terms in the traditional sliding mode guidance law is designed based on the sliding mode variable structure theory and neural network. A distance based cooperative control method is designed based on the guidance law, which is verified by simulation, The sliding mode guidance law based on RBF neural network gain adjustment can adjust the gain adaptively. Compared with the traditional sliding mode guidance law, the jitter of line of sight angular rate at the end can be effectively suppressed; The cooperative control method based on the guidance law can effectively ensure the distance between missiles, and hit high maneuvering targets.

Original languageEnglish
Title of host publicationProceedings of 2022 International Conference on Autonomous Unmanned Systems, ICAUS 2022
EditorsWenxing Fu, Mancang Gu, Yifeng Niu
PublisherSpringer Science and Business Media Deutschland GmbH
Pages392-400
Number of pages9
ISBN (Print)9789819904785
DOIs
Publication statusPublished - 2023
EventInternational Conference on Autonomous Unmanned Systems, ICAUS 2022 - Xi'an, China
Duration: 23 Sept 202225 Sept 2022

Publication series

NameLecture Notes in Electrical Engineering
Volume1010 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

ConferenceInternational Conference on Autonomous Unmanned Systems, ICAUS 2022
Country/TerritoryChina
CityXi'an
Period23/09/2225/09/22

Keywords

  • Coordinated control
  • Radial basis neural network
  • Sliding-mode guidance guidance law

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