• DOI:10.1007/S11431-015-5975-0
• Corpus ID: 54931221

Reaching a stochastic consensus in the noisy networks of linear MIMO agents: Dynamic output-feedback and convergence rate

@article{Wang2016ReachingAS,
  title={Reaching a stochastic consensus in the noisy networks of linear MIMO agents: Dynamic output-feedback and convergence rate},
  author={Yunpeng Wang and Long Cheng and Chenguang Yang and Zengguang Hou and Mingjia Tan},
  journal={Science China Technological Sciences},
  year={2016},
  volume={59},
  pages={45-54}
}

• Yunpeng Wang, L. Cheng, +2 authors M. Tan
• Published 2016
• Mathematics
• Science China Technological Sciences

This paper addresses the leader-following consensus problem of linear multi-agent systems (MASs) with communication noise. Each agent’s dynamical behavior is described by a linear multi-input and multi-output (MIMO) system, and the agent’s full state is assumed to be unavailable. To deal with this challenge, a state observer is constructed to estimate the agent’s full state. A dynamic output-feedback based protocol that is based on the estimated state is proposed. To mitigate the effect of communication noise, noise-attenuation gains are also introduced into the proposed protocol. In this study, each agent is allowed to have its own noise-attenuation gain. It is shown that the proposed protocol can solve the mean square leader-following consensus problem of a linear MIMO MAS. Moreover, if all noise-attenuation gains are of Θ(t-β), where β∈(0,1), the convergence rate of the MAS can be quantitatively analyzed. It turns out that all followers’ states converge to the leader’s state in the mean square sense at a rate of O(t-β).