有向图下非线性无人机群自适应合围控制

Distributed adaptive neural containment control for multi-UAV systems with nonlinear uncertainties under a directed graph

中文关键词  合围  无人机群系统  非线性不确定性  自适应神经网络控制  图论  反推法

英文关键词  containment  multi-UAV systems  nonlinear uncertainties  adaptive neural control  graph theory  backstepping

中文摘要

      本文研究了有向图下具有非线性和干扰的无人机群的分布式合围控制问题. 其中仅部分跟随者是领导者的邻 居, 对于每一个跟随者, 至少存在一条从领导者到这个跟随者有向路径. 文中假设无人机的空气动力学特性是非线性不 确定的, 并且领导者的输出是时变的. 结合反推设计方法提出了仅利用邻居信息的分布式合围控制方法, 使得跟随者的 状态收敛于领导者状态所张成的凸包里. 利用神经网络函数逼近技术补偿无人机系统中的非线性不确定项, 通过李雅普 诺夫稳定性理论证明了合围误差可以以任意收敛速度收敛到原点任意小的邻域. 最后通过仿真结果验证了控制协议的 有效性.

英文摘要

      We investigate the distributed containment control problem for multiple unmanned aerial vehicles (UAVs) systems with nonlinear uncertainties and bounded disturbances under a directed graph, where the leaders are neighbors of only a subset of the followers. For each follower, there exists at least one leader that has a directed path to the follower. It is assumed that aerodynamic characteristics of UAVs are nonlinear uncertainties, and the outputs of leaders are timevarying. A distributed containment control protocol combined with backstepping design method is proposed by using neighbors’information, so that the states of the followers will converge to the convex hull spanned by the dynamic leaders. The function approximation technique using neural networks is employed to compensate unknown nonlinear terms induced from the controller design procedure. By Lyapunov stability theorem, it is shown that the containment control errors will converge to an expected neighborhood of the origin with an arbitrary convergence rate. Simulation examples are presented to illustrate the effectiveness of the proposed control algorithm.