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This study introduces an adaptive event-triggered control strategy for multi-agent systems, reducing data transmission by minimizing unnecessary communication. This enhances resource efficiency and improves system consensus performance.

Keywords:
Adaptive event-triggered strategyIT-2 fuzzy modelMemory controllerMulti-agent systemsTime-varying topology

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Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Networked Systems

Background:

  • Multi-agent systems (MAS) require efficient communication strategies to manage onboard resources.
  • Time-varying topologies and uncertain parameters in MAS pose significant control challenges.
  • Event-triggered control offers a resource-saving alternative to traditional periodic control.

Purpose of the Study:

  • To design an event-triggered observer-based heterogeneous memory controller for leader-following MAS.
  • To develop a novel adaptive event-triggered strategy to reduce data transmission.
  • To enhance consensus performance in MAS with complex, uncertain dynamics.

Main Methods:

  • A nonlinear transformation law for estimation error is used for adaptive event-triggering.
  • Interval type-2 fuzzy models are employed to represent general time-varying topologies.
  • Heterogeneous fuzzy-dependent controllers incorporating past state measurements are designed.

Main Results:

  • Sufficient conditions for observer and controller design are derived, ensuring system consensus.
  • The proposed event-triggered strategy effectively reduces data transmission by avoiding unnecessary updates.
  • The inclusion of past state measurements demonstrably improves consensus control performance.

Conclusions:

  • The developed event-triggered control strategy is effective for leader-following MAS with time-varying topologies.
  • The adaptive mechanism conserves resources while maintaining desired system performance.
  • The controller design, incorporating past states and fuzzy logic, enhances overall consensus achievement.