Model-free adaptive consensus design for a class of unknown heterogeneous nonlinear multi-agent systems with packet...

Area of Science:

• Control Systems Engineering
• Networked Systems
• Nonlinear Dynamics

Background:

• Consensus is crucial for coordinated behavior in multi-agent systems.
• Unknown dynamics and random packet dropouts pose significant challenges to achieving consensus.
• Existing model-free adaptive control methods often require more data or complex stability analyses.

Purpose of the Study:

• To develop novel model-free adaptive consensus protocols for unknown heterogeneous nonlinear multi-agent systems.
• To address the challenges posed by random packet dropouts in communication networks.
• To design protocols requiring only local agent input/output data.

Main Methods:

• Dynamic linearization technique for system transformation.
• Data compensation mechanism to handle packet dropouts.
• A Squeeze Theorem-based method for stability analysis, replacing traditional contraction mapping principles.

Main Results:

• Successfully designed model-free adaptive consensus protocols for both leaderless and leader-following scenarios.
• Demonstrated that consensus can be achieved even with random packet dropouts.
• Validated the effectiveness of the Squeeze Theorem-based stability analysis.

Conclusions:

• The proposed model-free adaptive protocols effectively achieve consensus in unknown heterogeneous nonlinear multi-agent systems with random packet dropouts.
• The approach simplifies control design by utilizing only neighborhood data.
• The novel stability analysis method provides a robust theoretical foundation for model-free adaptive control in complex systems.