Learning-Based Robust Bipartite Consensus Control for a Class of Multiagent Systems

This article studies the robust bipartite consensus problems for heterogeneous nonlinear nonaffine discrete-time multiagent systems (MASs) with fixed and switching topologies against data dropout unknown disturbances. The controlled system’s virtual linear model is first developed by employing pseudopartial derivative technique, a distributed combined measurement error function established utilizing signed graph theory. Then, an input gain compensation scheme formulated to mitigate effects of in both feedback forward channels. Moreover, data-driven learning-based control (LRBCC) based on radial basis neural network observer proposed estimate disturbance, using online input/output without requiring any information mathematical dynamics. stability analysis LRBCC approach given. Finally, simulation hardware testing results further demonstrate designed method’s correctness effectiveness.