Data-based bipartite formation control for multi-agent systems with communication constraints
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel quantized data-driven control for multi-agent systems, ensuring accurate bipartite formation despite communication limits and dynamic network structures.
Area Of Science
- Control Systems Engineering
- Distributed Systems
- Networked Multi-Agent Systems
Background
- Multi-agent systems (MAS) face challenges in achieving coordinated behaviors under communication constraints.
- Bipartite formation control is crucial for cooperative-competitive interactions in MAS.
- Existing methods often require full system dynamics or lack robustness to topology changes.
Purpose Of The Study
- To develop a data-driven distributed bipartite formation control strategy for discrete-time MAS.
- To address communication constraints, including quantization and saturation.
- To accommodate both fixed and switching communication topologies in MAS.
Main Methods
- A quantized data-driven distributed bipartite formation control approach is proposed.
- Dynamic linearization is used to establish time-varying linear data models for agents.
- Control scheme constructed using incomplete input-output data, without requiring explicit system dynamics.
Main Results
- The proposed algorithm guarantees convergence of bipartite formation tracking errors to the origin.
- The approach is effective even with time-varying switching communication topologies.
- Simulation and hardware tests validate the scheme's performance.
Conclusions
- The developed quantized data-driven control is effective for distributed bipartite formation in MAS.
- The method offers robustness against communication constraints and dynamic network topologies.
- This work advances control strategies for complex networked systems.
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