Distributed Output Consensus for Heterogeneous Multiagent Systems With Markov Packet Loss

Area of Science:

• Control Theory
• Networked Systems
• Distributed Systems

Background:

• Investigates mean-square output consensus for heterogeneous linear multiagent systems (MASs).
• Addresses challenges posed by random packet loss channels, moving beyond independent and identically distributed (i.i.d.) assumptions.
• Models packet losses using a discrete-time Markov process to capture temporal correlations.

Purpose of the Study:

• Develop novel control schemes for MASs operating over random packet loss channels with temporal correlations.
• Achieve mean-square output consensus for heterogeneous linear systems under non-i.i.d. packet loss conditions.
• Ensure reliable distributed observer consensus despite communication uncertainties.

Main Methods:

• Considers two packet loss scenarios: identical and nonidentical.
• Derives analytical and numerical consensus conditions for identical packet losses.
• Employs edge Laplacian theory for nonidentical packet losses to decouple loss processes from topology.
• Designs output-regulation-based controllers for achieving mean-square output consensus.

Main Results:

• Presents consensus conditions revealing the interplay of packet loss rate, communication topology, and system dynamics.
• Provides computationally tractable numerical conditions for identical packet losses.
• Develops controllers guaranteeing mean-square output consensus for both identical and nonidentical packet loss scenarios.
• Validates findings through numerical simulations.

Conclusions:

• Successfully addresses the mean-square output consensus problem for heterogeneous MASs under complex packet loss conditions.
• Demonstrates the effectiveness of novel control schemes in achieving consensus over Markovian packet loss channels.
• Highlights the importance of considering temporal correlations in packet loss for robust control system design.