NN event-triggered finite-time consensus control for uncertain nonlinear Multi-Agent Systems with dead-zone input and...

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Robotics

Background:

• Multi-Agent Systems (MASs) face performance degradation due to unknown actuator failures and dead-zone nonlinearities.
• Ensuring system stability and synchronization in MASs under such uncertainties is a significant challenge.

Purpose of the Study:

• To develop a robust finite-time consensus control strategy for uncertain nonlinear MASs.
• To address and compensate for simultaneous dead-zone input and unknown actuator failures.
• To implement an event-triggered mechanism for efficient communication.

Main Methods:

• Utilized backstepping technology to construct finite-time adaptive controllers.
• Employed Neural Network (NN) control to manage unknown nonlinear dynamics.
• Developed an event-triggered control mechanism to reduce communication load.

Main Results:

• Achieved finite-time synchronization for all follower agents in MASs.
• Successfully compensated for unknown actuator failures and dead-zone input.
• Demonstrated the effectiveness of the proposed control method through simulations.

Conclusions:

• The proposed NN event-triggered finite-time consensus control method is effective for uncertain nonlinear MASs.
• The control strategy ensures robust finite-time synchronization despite system uncertainties and communication constraints.
• This approach offers a viable solution for practical MAS applications with actuator issues.