Event-triggered prescribed settling time consensus control of uncertain nonlinear multiagent systems with given...

Area of Science:

• Control Theory
• Artificial Intelligence
• Robotics

Background:

• Multiagent systems (MASs) are crucial in applications like UAV formations and coordinated robotics.
• Achieving consensus and managing communication load in MASs remain significant challenges.
• Existing methods often struggle with unknown dynamics and transient performance requirements.

Purpose of the Study:

• To design a prescribed-time consensus control strategy for MASs with guaranteed transient performance.
• To address unknown nonlinear dynamics within the MAS using advanced neural network techniques.
• To reduce communication overhead through an event-triggered control approach.

Main Methods:

• Nonlinear transformation of synchronization error using a designed performance function.
• Application of Radial Basis Function Neural Networks (RBFNNs) to handle system uncertainties.
• Development of an event-triggered mechanism to optimize communication resource usage.

Main Results:

• Synchronization error converges to a predefined interval within a setting time.
• The MAS achieves uniformly bounded convergence under the proposed control.
• The event-triggered mechanism effectively reduces communication load without compromising performance.

Conclusions:

• The proposed control strategy ensures fast and reliable consensus in MASs.
• RBFNNs provide a robust solution for unknown dynamics in consensus problems.
• Event-triggered control is a viable method for enhancing communication efficiency in MASs.