Multiple-bipartite consensus for networked Lagrangian systems without using neighbours' velocity information in the directed graph
Tiehui Zhang, Qiuxiang Liu, Jingyi Liu, Zhaoyan Wang, Hengyu Li
Abstract
This paper investigates the multiple-bipartite consensus problem without considering neighbours' velocity information in networked Lagrangian systems (NLSs). A distributed adaptive control algorithm without using neighbours' velocity information is proposed, which facilitates the practical configuration deployments in the coopetition networks. By borrowing a subtle vector composed of the eigenvector components associated with zero eigenvalue of Laplacian matrix, a novel reference estimated vector is introduced to conduct the stability analysis step-by-step in the coopetition networks. Finally, simulations are provided to show the effectiveness of the proposed algorithm.
Topics & Concepts
Bipartite graphLaplacian matrixEigenvalues and eigenvectorsLagrangianConsensus algorithmGraphDirected graphStability (learning theory)MathematicsComputer scienceTheoretical computer scienceMathematical optimizationAlgorithmApplied mathematicsQuantum mechanicsMachine learningPhysicsDistributed Control Multi-Agent SystemsNeural Networks Stability and SynchronizationNonlinear Dynamics and Pattern Formation