Antagonistic Interaction-Based Bipartite Consensus Control for Heterogeneous Networked Systems

This article investigates the bipartite consensus tracking control problem for nonlinear networked systems with antagonistic interactions and unknown backlash-like hysteresis. The generalized networked multiagent systems model is considered, in which every agent is an independent individual, and this model allows competitive and cooperative interactions to coexist. A Gaussian function is applied to simulate competition and cooperation among agents. Radial basis function (RBF) neural network (NN) is applied to estimate the unknown nonlinear function. By using backstepping technology, we propose an adaptive neural control protocol, which not only ensures that in the closed-loop system all the signals are bounded but also realizes bipartite consensus control. Finally, we present a simulation example to illustrate the effectiveness of the obtained result.

Automated summary

This article investigates the problem of bipartite consensus tracking control for nonlinear networked systems. It proposes an adaptive control protocol based on neural networks and backstepping technology, which ensures the boundedness of signals in the closed-loop system and achieves bipartite consensus control.