Adaptive Neural Consensus Tracking for Nonlinear Multiagent Systems Using Finite-Time Command Filtered Backstepping
针对不确定非线性多智能体系统，提出一种基于神经网络的分布式自适应有限时间控制方案，利用改进的指令滤波反步法实现有限时间一致性跟踪，且仅需更新每个智能体的一个参数，计算简单。
This paper is concerned with the finite-time consensus tracking control problems of uncertain nonlinear multiagent systems. A neural network-based distributed adaptive finite-time control scheme is developed, which can guarantee the consensus tracking is achieved in finite time with sufficient accuracy in the presence of unknown mismatched nonlinear dynamics. Such a finite-time feature is achieved by the modified command filtered backstepping technique based on the high-order sliding mode differentiator. Moreover, the proposed control scheme is completely distributed, since the control laws only use the local information. In addition, although mismatched uncertainty nonlinear dynamics are considered, only one parameter needs to be updated for each agent in the control scheme, which will simply the computations and make the proposed scheme more effective for applications. An example is included to verify the presented method.