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Scaled Position Consensus of High-Order Uncertain Multiagent Systems Over Switching Directed Graphs.
IEEE Transactions on Cybernetics
|October 12, 2023
Summary
This study achieves scaled position consensus in complex multiagent systems. A novel distributed adaptive algorithm ensures agents reach scaled consensus despite uncertainties and switching network topologies.
Area of Science:
- Control Theory
- Networked Systems
- Robotics
Background:
- Achieving consensus in multiagent systems is crucial for coordinated behavior.
- High-order systems and directed graphs present significant control challenges.
- Scaled position consensus, where agents agree on scaled states, is less explored.
Purpose of the Study:
- To develop a distributed adaptive algorithm for scaled position consensus in high-order multiagent systems.
- To address challenges including parametric uncertainties, switching directed graphs, and limited information exchange.
- To enable fully distributed control without shared or global gains.
Main Methods:
- Design of a high-order reference model for each agent using relative scaled position information.
- Proposal of a transformation to simplify scaled position consensus from high-order to first-order systems.
- Development of a distributed adaptive algorithm based on an MRACon scheme.
Main Results:
- Theoretical analysis confirms the achievement of scaled consensus over switching directed graphs.
- Numerical simulations validate the proposed algorithm's effectiveness.
- Demonstration of collective behaviors like traditional, bipartite, and cluster consensus by adjusting agent scales.
Conclusions:
- The proposed distributed adaptive algorithm effectively achieves scaled position consensus in complex multiagent systems.
- The method is robust to parametric uncertainties and operates over uniformly jointly connected switching directed graphs.
- The approach facilitates diverse collective behaviors through precise scale selection.
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