Fully Distributed Adaptive NN-Based Consensus Protocol for Nonlinear MASs: An Attack-Free Approach

Yuezu Lv, Jialing Zhou, Guanghui Wen*, Xinghuo Yu, Tingwen Huang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)

Abstract

This article works on the consensus problem of nonlinear multiagent systems (MASs) under directed graphs. Based on the local output information of neighboring agents, fully distributed adaptive attack-free protocols are designed, where speaking of attack-free protocol, we mean that the observer information transmission via communication channel is forbidden during the whole course. First, the fixed-time observer is introduced to estimate both the local state and the consensus error based on the local output and the relative output measurement among neighboring agents. Then, an observer-based protocol is generated by the consensus error estimation, where the adaptive gains are designed to estimate the unknown neural network constant weight matrix and the upper bound of the residual error vector. Furthermore, the fully distributed adaptive attack-free consensus protocol is proposed by introducing an extra adaptive gain to estimate the communication connectivity information. The proposed protocols are in essence attack-free since no observer information exchange among agents is undertaken during the whole process. Moreover, such a design structure takes the advantage of releasing communication burden.

Original languageEnglish
Pages (from-to)1561-1570
Number of pages10
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume33
Issue number4
DOIs
Publication statusPublished - 1 Apr 2022
Externally publishedYes

Keywords

  • Adaptive attack-free protocol
  • Consensus
  • Distributed fixed-time observer
  • Neural network (NN) approximation

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Lv, Y., Zhou, J., Wen, G., Yu, X., & Huang, T. (2022). Fully Distributed Adaptive NN-Based Consensus Protocol for Nonlinear MASs: An Attack-Free Approach. IEEE Transactions on Neural Networks and Learning Systems, 33(4), 1561-1570. https://doi.org/10.1109/TNNLS.2020.3042821