Hierarchical Variable Structure Control for the Path Following and Formation Maintenance of Multi-agent Systems

Hsiu Ming Wu, Mansour Karkoub

Research output: Contribution to journalArticle

1 Citation (Scopus)


In this paper, a hierarchical variable structure control (HVSC) is proposed for path following and formation maintenance of multi-agent systems (MAS) based on the one leader and one follower (1L-1F) configuration. In terms of the leader, the path following of a nonholonomic mobile robot (NMR) can be regarded as tracking of a virtual reference NMR. Then, a feedback control law is used to attain path following based on kinematics of the NMR. Subsequently, a variable structure controller (VSC) with known upper bound of the disturbances is designed to achieve velocity control based on the dynamics of the NMR. Furthermore, as far as the formation maintenance is concerned, a method of feedback linearization is utilized to exponentially stabilize the relative distance and orientation between the leader and follower. Similarly, a VSC is also designed for complete velocity control such that the aforementioned formation is maintained. The proposed method with hierarchical structure simultaneously attains the path following of the leader and maintains the formation for the MAS. In addition, the robustness of the proposed scheme is guaranteed in spite of persistent disturbances and the stability analysis of the closed-loop system is proved via Lyapunov stability criteria. Finally, computer simulations are conducted to validate the feasibility and effectiveness of the proposed control scheme.

Original languageEnglish
Pages (from-to)267-277
Number of pages11
JournalJournal of Intelligent and Robotic Systems: Theory and Applications
Issue number2
Publication statusPublished - 15 Aug 2019


  • Hierarchical variable structure control
  • Leader-follower configuration
  • Lyapunov stability criteria
  • Multi-agent systems
  • Nonholonomic mobile robot
  • Virtual reference NMR

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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