Human-In-The-Loop Adaptive Bipartite Tracking Consensus of Networked Euler-Lagrange Systems

This paper investigates the human-in-the-loop adaptive practical bipartite tracking consensus issue for networked Euler-Lagrange Systems (ELSs). The human-in-the-loop system is considered to generate reference signals for all the followers to track. Moreover, the ELSs are subject to unknown external disturbances. At first, a distributed observer is designed to estimate the state of the human-in-the-loop system. Furthermore, an adaptive sliding mode controller is designed to achieve the practical bipartite consensus tracking (BCT) while enhancing the robustness of the network system. The efficacy of the proposed control scheme is verified through simulation results.