Abstract
The bipartite consensus problem of networked robotic systems (NRSs) with parametric uncertainties, input disturbances, and quantized-data interactions is addressed in this paper. Some novel distributed estimator-based control algorithms are designed to guarantee that all controlled robots can eventually reach bipartite consensus or their states asymptotically converge to the origin. By employing the Lyapunov argument and nonsmooth analysis theory, several sufficient criteria on control parameters for stabilizing the closed-loop systems and solving the aforementioned problems are provided. Finally, simulation examples are presented to illustrate the proposed algorithms.
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