Neural-Network-Based Adaptive Tracking Control for Nonlinear Multiagent Systems: The Observer Case
针对状态不可测且受未知干扰的高阶非线性多智能体系统，设计了基于径向基神经网络的自适应跟踪控制方案，结合复合扰动观测器和状态观测器，并扩展到事件触发情形以节省控制资源。
This article focuses on the neural-network (NN)-based adaptive tracking control issue for a class of high-order nonlinear multiagent systems both subjected to the immeasurable state variables and unknown external disturbance. Combining with the radial basis function NNs (RBF NNs), the composite disturbance observer and state observer for each follower are established, respectively. The purpose of this work is to develop NN-based adaptive tracking control schemes such that the output of each follower ultimately tracks that of the leader and all the signals of the closed-loop systems are semiglobally uniformly ultimately bounded by utilizing the backstepping technique. Furthermore, so as to cope with the sparsity of the control resources, the proposed method is extended to the event-triggered case and the adaptive event-triggered tracking control protocol is formulated for nonlinear multiagent systems. Finally, the numerical example is performed to verify the efficacy of the proposed approach.