无需相对速度信息的多Euler-Lagrange 系统自适应神经网络包含控制

对含有模型非线性不确定性和外部扰动的多Euler-Lagrange 系统的分布式协调包含控制问题进行研究. 考虑通讯拓扑为有向图, 所有领航者均为动态, 且各智能体间相对速度信息不可测情况. 首先, 选取相对速度作为辅助变量, 引入低通滤波器进行估计; 然后, 采用神经网络方法逼近并补偿非线性不确定性, 提出一种分布式自适应包含控制律, 并应用Lyapunov 稳定性理论证明闭环系统的包含误差一致最终有界; 最后, 通过仿真算例验证了所提出的控制律的有效性.

Adaptive neural-network containment control of multiple Euler-Lagrange systems without using relative velocity information

The problem of distributed coordinated containment control of multiple Euler-Lagrange systems in presence of nonlinear model uncertainties and external disturbances is investigated. The communication topology is a directed graph, the leaders are dynamic and the relative velocity information between agents is unmeasured. Firstly, the relative velocities are chosen as the auxiliary variables and estimate the auxiliary variables through the low-pass filters. Then, the neural-network method is used to approximate and compensate the nonlinear uncertainties of the systems, and the distributed adaptive containment algorithm is proposed. Based on the Lyapunov stability theory, it is proved that the containment errors between leaders and followers are uniformly ultimately bounded. Finally, a simulation example is presented to illustrate the effectiveness of the proposed control method.