Leader-Following Consensus Control for Uncertain Feedforward Stochastic Nonlinear Multiagent Systems

Kuo Li, Changchun Hua, Xiu You, Choon Ki Ahn

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


This article addresses the leader-following consensus problem of feedforward stochastic nonlinear multiagent systems with switching topologies. Output information for all agents, except for state information, can be acquired based on sensor measurement. Moreover, the stochastic disturbances from external unpredictable environments are considered on all agent systems with a feedforward structure. In these conditions, we propose a novel consensus scheme with a simple design procedure. First, for each follower, we construct a dynamic gain-based switched compensator using its output and its neighbor agents' outputs to provide feedback control signals. Then, for each follower, we develop a compensator-based distributed controller that is not directly associated with the topology switching signal such that it has a first derivative and antishake. Thereafter, by means of the Lyapunov stability theory, we verify that the leader-following consensus can be acquired asymptotically in probability under the controllers' action if the topology switching signal fulfills an average dwell time condition. Finally, the feasibility of the control algorithm is checked via numerical simulation.

Original languageEnglish
Pages (from-to)1049-1057
Number of pages9
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number2
Publication statusPublished - 2023 Feb 1

Bibliographical note

Funding Information:
This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB1308300, in part by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (Ministry of Science and ICT) under Grant NRF-2020R1A2C1005449, in part by the National Natural Science Foundation of China under Grant U20A20187 and Grant 618255304, in part by the Science Fund for Creative Research Groups of Hebei Province under Grant F2020203013, in part by the Science and Technology Development Grant of Hebei Province under Grant 20311803D and Grant 19011824Z, in part by the National Defence Fundamental Project under Grant 2020A130, and in part by the Post-Doctoral Innovative Talent Support Program of China under Grant BX20200201.

Publisher Copyright:
© 2022 IEEE.


  • Feedforward stochastic multiagent systems
  • output-feedback consensus control
  • switched compensators
  • switching topologies

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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