考虑时延的多Euler-Lagrange系统自适应神经网络协调跟踪控制

考虑存在通讯时延,在有向通讯拓扑结构下研究多Euler-Lagrange系统的协调跟踪控制问题。仅有部分跟随者可以获得静态领航者信息。对每一个跟随者设计了一种分布式观测器,以获得领航者的状态量。针对系统模型具有非线性不确定性和外部扰动情况,基于神经网络方法提出了两种分布式自适应协调控制律,分别使每一个跟随者对领航者的跟踪误差最终有界和渐近收敛到零。运用Lyapunov稳定性理论对两种控制律的稳定性进行了证明。数值仿真验证了本文提出的控制律的有效性。

Adaptive neural-network coordinated tracking control of multiple Euler-Lagrange systems with communication delays

Considering communication delays, the coordinated tracking control problems of multiple Euler-Lagrange systems under a directed graph are investigated. The information of the static leader is available to only a subset of the followers. First, the distributed observer is designed so that the state information of the leader can be estimated by every follower. Then, considering model nonlinear uncertainties and external disturbances, two distributed adaptive control algorithms based on neural networks are proposed to make sure that the tracking errors for every follower can be bounded and asymptotically convergent, respectively. The closed-loop systems are investigated by using the Lyapunov theory and the graph theory. Numerical simulations are provided to verify the effectiveness of the proposed methods.