Distributed Model-Free Bipartite Consensus Tracking for Unknown Heterogeneous Multi-Agent Systems with Switching...

Area of Science:

• Control Theory
• Robotics
• Networked Systems

Background:

• Multi-agent systems (MASs) often require coordinated behavior for complex tasks.
• Achieving consensus in heterogeneous MASs with unknown dynamics and switching topologies is challenging.
• Existing methods may rely on precise system models, limiting their applicability.

Purpose of the Study:

• To develop a distributed model-free adaptive bipartite consensus tracking (DMFABCT) scheme.
• To enable tracking of both time-invariant and time-varying trajectories in unknown discrete-time MASs.
• To address systems with switching topologies and coopetition networks.

Main Methods:

• Utilizing a pseudo partial derivative (PPD) approach to estimate an equivalent dynamic linearization data model.
• Employing only input-output (I/O) data from each agent, eliminating the need for a precise mathematical model.
• Investigating cooperative interactions within the multi-agent system.

Main Results:

• The proposed DMFABCT scheme achieves bipartite consensus tracking for unknown dynamic discrete-time heterogeneous MASs.
• The scheme demonstrates effectiveness and robustness even with switching topologies.
• Rigorous proof confirms the convergent property, leading to reduced trajectory errors.

Conclusions:

• The novel DMFABCT scheme offers a model-free solution for complex consensus tracking problems.
• The approach is suitable for heterogeneous multi-agent systems with dynamic and topological uncertainties.
• Simulations validate the effectiveness and robustness of the proposed DMFABCT scheme.