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Adaptive fuzzy fixed-time bipartite consensus control for stochastic nonlinear multi-agent systems with performance

Lei Yan1, Junhe Liu2, Guanyu Lai2

  • 1School of Intelligent Manufacturing, Nanyang Institute of Technology, Nanyang, Henan, 473004, China; School of Automation, Guangdong University of Technology, Guangzhou, Guangdong, 510006, China.

ISA Transactions
|August 2, 2024
PubMed
Summary
This summary is machine-generated.

This study presents a fixed-time bipartite consensus control for stochastic nonlinear multi-agent systems (MASs). The proposed method ensures performance constraints are met, achieving stability and prescribed accuracy in finite time.

Keywords:
Adaptive fuzzy controlBipartite consensusFixed-time controlPerformance constraints

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Area of Science:

  • Control Theory
  • Systems Engineering
  • Artificial Intelligence

Background:

  • Multi-agent systems (MASs) are crucial in distributed control.
  • Stochastic nonlinear systems present significant control challenges.
  • Bipartite consensus and fixed-time control are advanced control objectives.

Purpose of the Study:

  • Investigate fixed-time bipartite consensus control for stochastic nonlinear MASs.
  • Address performance constraints including steady-state accuracy and settling time.
  • Develop an adaptive fuzzy controller for robust performance.

Main Methods:

  • Constraint scaling function to model performance constraints.
  • Error transformation and barrier functions for fixed-time convergence.
  • Adaptive fuzzy logic systems to handle system uncertainties.

Main Results:

  • Guaranteed practical fixed-time stability in probability.
  • Consensus error within user-defined performance constraints.
  • Effectiveness validated through simulation results.

Conclusions:

  • Successfully developed a fixed-time bipartite consensus control scheme.
  • The controller ensures prescribed performance and stability for MASs.
  • The approach is effective for stochastic nonlinear systems with uncertainties.