Disturbance Observer-Based Neural Network Nonsingular Fixed-Time Adaptive Consensus Control for Uncertain Nonlinear Multiagent Systems

This article aims to investigate the neural network (NN) nonsingular fixed-time adaptive consensus control issue for nonlinear multiagent systems (MASs) with parameter uncertainties. By introducing a generalized intermediate-variable-based disturbance observer (IVBDO), a novel distributed fixed-time NN adaptive controller is constructed based on the quartic Lyapunov function method. Under this protocol, the mismatched external disturbances of each agent are real-time online estimated; meanwhile, the singularity phenomenon during the fixed-time design process can be effectively eliminated. The presented control algorithm not only guarantees that the controlled system is semi-globally uniformly ultimately bounded (SGUUB) but also that the distributed output tracking errors converge to an adjustable compact set of the origin within a fixed-time interval. Simulation results are displayed to check the effectiveness of the suggested approach.