Minimum-energy distributed consensus of uncertain agents

This paper presents a consensus algorithm for a multi-agent system where each agent has access to its imperfect own state and neighboring state measurements. The measurements are subject to deterministic disturbances and the proposed algorithm provides a minimum-energy estimate of the measured states which is instrumental in achieving consensus by the nodes. It is shown that the proposed consensus algorithm converges exponentially in the absence of disturbances, and its performance under bounded continuous disturbances is investigated as well. The convergence performance of the proposed method is further studied using simulations where we show that consensus is achieved despite using large measurement errors.

[1]  João Pedro Hespanha,et al.  Minimum-energy state estimation for systems with perspective outputs , 2006, IEEE Transactions on Automatic Control.

[2]  Jonathan H. Manton,et al.  Stochastic Consensus Seeking With Noisy and Directed Inter-Agent Communication: Fixed and Randomly Varying Topologies , 2010, IEEE Transactions on Automatic Control.

[3]  Shreyas Sundaram,et al.  Distributed Function Calculation via Linear Iterative Strategies in the Presence of Malicious Agents , 2011, IEEE Transactions on Automatic Control.

[4]  Magnus Egerstedt,et al.  Graph Theoretic Methods in Multiagent Networks , 2010, Princeton Series in Applied Mathematics.

[5]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[6]  Bassam Bamieh,et al.  Coherence in Large-Scale Networks: Dimension-Dependent Limitations of Local Feedback , 2011, IEEE Transactions on Automatic Control.

[7]  S. Kahne,et al.  Optimal control: An introduction to the theory and ITs applications , 1967, IEEE Transactions on Automatic Control.

[8]  Richard M. Murray,et al.  Consensus problems in networks of agents with switching topology and time-delays , 2004, IEEE Transactions on Automatic Control.

[9]  J. Willems.  Deterministic least squares filtering , 2004 .

[10]  Francesco Bullo,et al.  Distributed Control of Robotic Networks , 2009 .

[11]  R. Mortensen.  Maximum-likelihood recursive nonlinear filtering , 1968 .

[12]  Naomi Ehrich Leonard,et al.  Rearranging trees for robust consensus , 2011, IEEE Conference on Decision and Control and European Control Conference.

[13]  Frank L. Lewis,et al.  Distributed robust consensus control of multi-agent systems with heterogeneous matching uncertainties , 2013, Autom..

[14]  R. Olfati-Saber,et al.  Consensus Filters for Sensor Networks and Distributed Sensor Fusion , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[15]  Reza Olfati-Saber,et al.  Consensus and Cooperation in Networked Multi-Agent Systems , 2007, Proceedings of the IEEE.

[16]  Robert E. Mahony,et al.  Near-Optimal Deterministic Filtering on the Rotation Group , 2011, IEEE Transactions on Automatic Control.

[17]  Laura Giarré,et al.  Consensus for Networks with Unknown but Bounded Disturbances , 2009, SIAM J. Control. Optim..