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Cooperative Tracking Control for Nonlinear MASs Under Event-Triggered Communication.

Wei-Wei Che, Lili Zhang, Chao Deng

IEEE Transactions on Cybernetics

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Summary

This study introduces a new method for cooperative tracking in nonlinear multiagent systems (MASs) with intermittent communication. The approach ensures stable tracking and bounded system signals, even without continuous data exchange.

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Area of Science:

Control Systems Engineering
Artificial Intelligence
Robotics

Background:

Cooperative tracking in nonlinear multiagent systems (MASs) is crucial for coordinated behaviors.
Standard adaptive backstepping methods fail with discontinuous communication due to signal discontinuities.
Event-triggered communication in MASs presents challenges for traditional control designs.

Purpose of the Study:

To develop a robust control strategy for cooperative tracking in nonlinear MASs with event-triggered communication.
To overcome the limitations of continuous communication requirements in existing backstepping methods.
To enhance tracking performance and signal boundedness in decentralized MASs.

Main Methods:

A hierarchical design combining distributed cooperative estimators and neural network-based decentralized controllers.
Introduction of a dynamic event-triggered mechanism for parameter estimation.
Utilizing interpolation polynomial method to create smooth estimators with high-order derivatives for backstepping applicability.
Designing a backstepping-based decentralized neural network tracking controller.

Main Results:

The proposed method ensures asymptotic convergence of tracking errors.
All signals within the closed-loop systems are demonstrated to be bounded.
Achieved superior asymptotic tracking performance compared to existing event-triggered communication methods for MASs.
Considered a more general class of nonlinear MASs.

Conclusions:

The developed hierarchical scheme effectively addresses cooperative tracking in nonlinear MASs with discontinuous communication.
The integration of smooth estimators and neural network controllers guarantees system stability and performance.
The method offers a significant advancement for event-triggered control in multiagent systems.