Decentralized Synchronization of Uncertain Nonlinear Systems With a Reputation Algorithm

Decentralized synchronization approaches generally assume that communication of state information between neighboring agents is completely accurate. However, just one agent's communication of inaccurate information to neighbors can significantly degrade the performance of the entire network. To help abate this problem, we present a decentralized controller for a leader-follower framework which uses local information to vet neighbors and change consensus weights accordingly. Because updates of the consensus weights produce a switched system, switching control theory techniques are used to develop a dwell-time that must elapse between agents' successive weight updates. A Lyapunov-based stability analysis is presented which develops sufficient conditions for approximate convergence of follower agents' states to a leader agent's time-varying state. Simulation results are provided to demonstrate the performance of the developed techniques.

[1]  Wei Ren,et al.  Multi-vehicle consensus with a time-varying reference state , 2007, Syst. Control. Lett..

[2]  Te-son Kuo,et al.  Trace bounds on the solution of the algebraic matrix Riccati and Lyapunov equation , 1986 .

[3]  B. Datta.  Numerical methods for linear control systems : design and analysis , 2004 .

[4]  John S. Baras,et al.  Using trust in distributed consensus with adversaries in sensor and other networks , 2014, 17th International Conference on Information Fusion (FUSION).

[5]  Philippe Thiran,et al.  Maintenance-based trust for multi-agent systems , 2009, AAMAS.

[6]  Kyandoghere Kyamakya,et al.  Recent Advances in Nonlinear Dynamics and Synchronization , 2009 .

[7]  Warren E. Dixon,et al.  Supporting Lemmas for RISE-based Control Methods , 2013, ArXiv.

[8]  Shreyas Sundaram,et al.  Consensus of multi-agent networks in the presence of adversaries using only local information , 2012, HiCoNS '12.

[9]  Baxter F. Womack,et al.  Comments on "On the Lyapunov matrix equation" , 1975 .

[10]  Warren E. Dixon,et al.  Asymptotic Synchronization of a Leader-Follower Network of Uncertain Euler-Lagrange Systems , 2013, IEEE Transactions on Control of Network Systems.

[11]  Daniel Liberzon,et al.  Switching in Systems and Control , 2003, Systems & Control: Foundations & Applications.

[12]  João Pedro Hespanha,et al.  Linear Systems Theory , 2009 .

[13]  Khashayar Khorasani,et al.  Team Cooperation in a Network of Multi-Vehicle Unmanned Systems: Synthesis of Consensus Algorithms , 2012 .

[14]  V. Mehrmann,et al.  Perturbation theory for matrix equations , 2003, IEEE Transactions on Automatic Control.

[15]  Frank L. Lewis,et al.  Decentralized trust-based self-organizing cooperative control , 2014, 2014 European Control Conference (ECC).

[16]  Prasant Mohapatra,et al.  Trust Computations and Trust Dynamics in Mobile Adhoc Networks: A Survey , 2012, IEEE Communications Surveys & Tutorials.

[17]  Romeo Ortega,et al.  Synchronization of Networks of Nonidentical Euler-Lagrange Systems With Uncertain Parameters and Communication Delays , 2011, IEEE Transactions on Automatic Control.

[18]  Henk Nijmeijer,et al.  Mutual synchronization of robots via estimated state feedback: a cooperative approach , 2004, IEEE Transactions on Control Systems Technology.

[19]  Warren E. Dixon,et al.  Concurrent learning-based network synchronization , 2014, 2014 American Control Conference.

[20]  Frank L. Lewis,et al.  Trust method for multi-agent consensus , 2012, Defense, Security, and Sensing.