Data-driven bipartite consensus control for multi-agent systems with data quantization
Authors: Zhao, H.R., Peng, L., Yu, H.N. and Shen, Y.H.
Journal: Kongzhi Lilun Yu Yingyong/Control Theory and Applications
In this paper, we investigate the data quantization problem of an unknown dynamics model of nonlinear discrete-time multi-agent systems (MASs) with collaborative and antagonistic relationships and propose a data-driven control algorithm for MASs to perform bipartite consensus tracking control. We first develop an estimation algorithm of the time-varying parameter by designing a performance index function through transforming the unknown dynamics model nonlinear agent into a data model with a time-varying parameter using the compact form dynamic linearization (CFDL) approach. We then design a quantized data-driven distributed bipartite consensus tracking control protocol based on the data model by employing the algebraic graph theory and the sector-bound approach. We also strictly prove the convergence property of the proposed algorithm. The results show that although the MASs subject to quantized data, the formulated protocol still guarantees the bipartite consensus tracking errors of MASs to converge to zero. Finally, the developed approach's effectiveness and robustness are further verified through a numerical example and a contrast experiment.