Neural-Network-Based Adaptive Consensus Control for Nonlinear Multiagent Systems Subject to Time Delays and Unknown Disturbance

The current investigation aims at the adaptive consensus issue for a class of nonlinear multiagent systems with time delays and unknown disturbances under a directed graph topology. A novel designed disturbance observer eliminates the impact on the consensus induced by unknown external disturbances. Furthermore, the Lyapunov–Krasovskii functional method is applied to tackle the time delays in the design process. With the approximation capability of neural networks, the adaptive controller is developed by the backstepping technique, which can alleviate the burden of computing and make the consensus tracking error converge to a small compact region. The control scheme proposed in this paper enables the nonlinear multiagent systems to achieve consensus output. Meanwhile, a numerical simulation example is performed to verify the effectiveness of the proposed scheme.