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Admissible consensus tracking control for nonlinear singular multi-agent systems via sampled-data event-triggered

Tong Yuan¹, Lin Li¹

¹Automation Division, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

ISA Transactions

|February 24, 2025

Summary

This study addresses consensus tracking for unknown nonlinear singular multi-agent systems using sampled-data event-triggered control. A novel adaptive protocol ensures admissible consensus, outperforming existing methods.

Keywords:

Admissibility Double estimation problem Event-triggered Sampled data Singular multi-agent systems

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Area of Science:

Control Theory
Systems Engineering
Networked Systems

Background:

Consensus tracking is crucial for multi-agent systems.
Event-triggered mechanisms reduce communication load.
Singular systems and unknown nonlinear dynamics present significant challenges.

Purpose of the Study:

To develop an admissible consensus tracking control strategy for nonlinear singular multi-agent systems with unknown dynamics.
To design and compare static and dynamic sampled-data event-triggered mechanisms.
To propose a distributed adaptive event-triggered control protocol to overcome double estimation issues.

Main Methods:

Design of static and dynamic event-triggered observers within a sampled-data framework.
Development of a distributed adaptive event-triggered control protocol.
Establishment of sufficient conditions for admissible consensus tracking.
Verification through three numerical examples.

Main Results:

The dynamic event-triggered mechanism demonstrates superior performance compared to the static one.
The proposed adaptive protocol effectively eliminates bounded consensus errors caused by double estimation.
Sufficient conditions for achieving admissible consensus tracking are established.

Conclusions:

The developed distributed adaptive event-triggered control protocol is effective for nonlinear singular multi-agent systems.
The proposed method achieves admissible consensus tracking under sampled-data conditions with unknown dynamics.
The findings offer a robust solution for complex multi-agent system coordination.