Fast Finite-Time Observer-Based Event-Triggered Consensus Control for Uncertain Nonlinear Multiagent Systems with...

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Networked Systems

Background:

• Uncertain nonlinear multiagent systems (MASs) present significant control challenges due to inherent uncertainties and state restrictions.
• Existing consensus control methods often struggle with fast convergence and resource efficiency in complex MASs.

Purpose of the Study:

• To develop a fast finite-time consensus control strategy for uncertain nonlinear multiagent systems.
• To address system uncertainties and unknown states using advanced approximation techniques.
• To enhance resource efficiency via an event-triggered control mechanism.

Main Methods:

• Utilized radial basis function neural networks (RBFNNs) for system uncertainty estimation.
• Proposed state and disturbance observers to approximate unknown system states and disturbances.
• Developed a fast finite-time consensus control algorithm.
• Implemented an event-triggered control strategy for resource optimization.

Main Results:

• Achieved fast finite-time stability for the multiagent systems.
• Ensured all system signals remain stable and bounded.
• Demonstrated the effectiveness of the event-triggered controller in saving resources.
• Validated the proposed approach through simulation examples.

Conclusions:

• The developed control strategy effectively achieves fast finite-time consensus in uncertain nonlinear multiagent systems.
• The integration of RBFNNs, observers, and event-triggered control offers a robust and efficient solution.
• The approach is validated by simulations, confirming its practical applicability.