Neural Network-Based Group-Bipartite Consensus for Multiple Euler-Lagrange Systems

This paper deals with the adaptive group-bipartite consensus (GBC) control problem for multiple Euler-Lagrange systems (MELSs) using neural network (NN) method. A sliding mode variable is first introduced to address both group consensus and bipartite consensus of MELSs, and NN method is then utilized to deal with the uncertainty of system model and external disturbance. Moreover, the stability of the closed-loop controlled system is ensured by using an appropriate Lyapunov function. Finally, the theoretical results are validated through numerical simulations.