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Quantized Consensus of Multi-Agent Networks With Sampled Data and Markovian Interaction Links.
IEEE Transactions on Cybernetics
|July 12, 2018
Summary
This study examines how quantization and sampled data affect consensus networks with a leader. It establishes conditions for reliable consensus tracking in networks with Markovian interaction links.
Area of Science:
- Control Systems Engineering
- Networked Systems
- Information Theory
Background:
- Consensus networks are crucial for distributed systems, but practical implementations face challenges.
- Quantization and sampled data introduce complexities in achieving coordinated behavior.
- Markovian interaction links add dynamic uncertainty to network topology.
Purpose of the Study:
- To investigate the combined impact of quantization, sampled data, and Markovian links on leader-following consensus networks.
- To develop conditions ensuring reliable state convergence and consensus tracking.
- To analyze the influence of network structure and coupling gains on consensus performance.
Main Methods:
- Derivation of a sufficient condition for encoded state convergence.
- Establishment of a necessary and sufficient condition for mean-square consensus tracking.
- Design of an optimal linear quadratic regulator (LQR)-based gain matrix.
- Analysis of the consensus region under varying network parameters.
Main Results:
- A sufficient condition guarantees convergence of encoded states in the network.
- A necessary and sufficient condition ensures mean-square consensus tracking.
- Two sufficient conditions for coupling gain are presented, including a fully distributed one.
- The analysis provides insights into the consensus region.
Conclusions:
- The proposed methods effectively address consensus tracking in complex networked systems.
- The theoretical results are validated through a numerical example.
- The findings contribute to the design of robust distributed control systems under communication constraints.
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