Adaptive Finite-Time Bipartite Output Consensus Tracking of Second-Order Nonlinear Multi-agent Systems with Input Saturation

This paper considers the finite-time adaptive consensus tracking of second-order nonlinear multi-agent systems with input saturation. The uncertain nonlinear multi-agent systems can achieve adaptive neural consensus tracking by using the fuzzy logic system to approximate the unknown nonlinear dynamics. In the control scheme, we design a differentiator to obtain both the intermediate signal and its differentiator in each step of the distributed backstepping for each agent. Construct errors compensation to eliminate filtering errors. The result and simulation demonstrate that this approach is effective.