Adaptive Fuzzy Event-Triggered Cooperative Control for Multi-Robot Systems: A Predefined-Time Strategy

Area of Science:

• Robotics
• Control Systems
• Artificial Intelligence

Background:

• Multi-robot systems require robust control strategies to handle external disturbances, input saturation, and model uncertainties.
• Efficient communication resource management is crucial for the scalability and practicality of multi-robot systems.
• Achieving precise and rapid convergence times in cooperative control remains a significant challenge.

Purpose of the Study:

• To propose a predefined-time adaptive fuzzy cooperative controller for multi-robot systems.
• To address challenges posed by external disturbances, input saturation, and model uncertainties.
• To enhance communication efficiency through an event-triggered mechanism.

Main Methods:

• Development of a predefined-time controller using an asymmetric tan-type barrier Lyapunov function for directed communication topologies.
• Implementation of predefined-time fuzzy logic systems to approximate external disturbances and model uncertainties.
• Improvement of a dynamic relative threshold event-triggered mechanism to conserve communication resources.

Main Results:

• The proposed controller ensures quick response and precise convergence times.
• Fuzzy logic systems effectively approximate system uncertainties and external disturbances.
• The event-triggered mechanism significantly reduces communication load.
• Predefined-time stability is rigorously proven using Lyapunov stability theorem.

Conclusions:

• The developed controller demonstrates effectiveness in managing complex conditions within multi-robot systems.
• The integration of fuzzy logic and event-triggering offers a promising approach for robust and efficient cooperative control.
• Simulation results validate the algorithm's performance and superiority over comparative methods.