Data-driven Bipartite Consensus Control for Multi-agent Systems with Sensor Saturation

This study proposes a data-driven control scheme for unknown nonlinear discrete time multi-agent systems (MASs) with sensor saturation to achieve bipartite consensus tracking. First, an unknown nonlinear MAS model is transmitted into a dynamic linearization data model with varying parameters through a pseudo partial derivative technique. An estimation approach of time-varying parameters is formulated by designing the corresponding performance index function. Afterward, a saturated data-based data-driven distributed bipartite consensus control protocol is proposed. The convergence of the proposed approach is strictly proved. Lastly, simulation experiments are carried out. Although an increase in sensor saturation causes the proposed control protocol's convergence rate to slow down, the proposed method can guarantee that the bipartite consensus error converges to zero.