绳系拖曳飞行器高抗扰轨迹跟踪控制

针对受未知风扰动作用下的绳系拖曳飞行器轨迹精确控制问题，设计了一种基于最小学习参数神经网络估计器的拖曳飞行器轨迹动态面控制方法。首先，结合绳系拖曳系统多刚体动力学模型，构建拖曳飞行器六自由度非线性模型，并完成其仿射非线性化处理。其次，考虑到拖曳飞行器可能受到前方飞机尾涡、紊流和阵风等未知气流及不可测量瞬变缆绳拉力等扰动的综合影响，构建了基于最小学习参数神经网络的拖曳飞行器状态/扰动在线估计器，以准确重构系统不可测量集总扰动。然后，基于所提状态/扰动在线估计器，设计了一种基于最小学习参数神经网络状态/扰动在线估计器的拖曳飞行器轨迹动态面控制方法，并分析了系统稳定性。最后，仿真表明，所提方法能够在多重气流扰动下实现拖曳飞行器位置稳定和机动轨迹跟踪。 

High anti-distu rbance trajectory tracking control for cable towed vehicle

To handle the precise trajectory control problem of the cable towed vehicle under unknown airflow disturbances, the minimal learning parameter neural network estimator based dynamic surface trajectory control method is proposed for the towed vehicle. Firstly, combined with the multi-body dynamic model of the cable towed system, the towed vehicle's six-degree-of-freedom nonlinear model is established and then formulated in the affine nonlinear form. Secondly, considering the comprehensive influence on the towed vehicle by the unknown airflow disturbances (such as the trailing vortex, atmospheric turbulence, gust, etc.) and the variably unmeasurable cable tensions, the minimal learning parameter neural network based state/disturbance online estimators are established to accurately reconstitute the unmeasurable lumped disturbance of system. Thirdly, on the basis of the above state/disturbance online estimators, the minimal learning parameter neural network state/disturbance estimator based dynamic surface trajectory control method is proposed. Finally, the simulation results show that the proposed method can achieve the towed vehicle's trajectory stabilization and maneuvering trajectory tracking control.