Adaptive Tracking Consensus Control of Nonlinear Multiagent Systems With Predefined Accuracy Under Disturbance Observer
针对存在扰动和输入饱和的非线性多智能体系统，提出一种自适应跟踪一致性控制方法，利用扰动观测器和神经网络处理未知干扰与非线性，确保跟踪误差收敛到预定精度。
This article aims to a predefined tracking precision consensus control issue for nonlinear uncertain multiagent systems (MASs) with disturbance and input saturation. Unlike the existing results of prespecified accuracy for MASs, the phenomenon of unknown control gains is solved in this article. A saturation model based on the Gaussian error function is applied due to the appearance of input saturation. The unknown disturbance is considered which can be solved by a disturbance observer. Also, to handle the problem of an unknown coefficient for the controller, the Nussbaum function is employed. Moreover, the radial basis function neural networks (RBF NNs) are utilized to estimate unknown nonlinear functions. On account of the Lyapunov stability method and backstepping technique, adaptive laws are created and the desired distributed controller is designed which guarantees that the consensus errors can converge to prescribed values. Finally, several simulation examples demonstrate the valid of the proposed method.