Disturbance Observer-Based Neural Network Nonsingular Fixed-Time Adaptive Consensus Control for Uncertain Nonlinear Multiagent Systems
针对参数不确定的非线性多智能体系统，提出一种基于扰动观测器的神经网络固定时间自适应控制器，能在固定时间内消除奇异现象并实现输出跟踪误差收敛。
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.