多无人直升机协作搬运控制技术研究

Research on Cooperative Transportation Control Technology with Multi-unmanned Helicopters

随着多无人机协同控制技术的迅猛发展,多无人机悬挂运输飞行成为国内外研究的热点。针对多无人直升机协作悬挂搬运的绳索摆动问题和轨迹控制问题,设计了基于最小学习参数神经网络的动态面轨迹控制方法。首先,建立了多无人直升机协作悬挂搬运系统的非线性动力学模型。然后,把对无人直升机动力学特性和稳定性能影响较大且无法测量的各绳索拉力作为扰动,构建最小学习参数神经网络估计器进行估计,并在控制设计中予以补偿。然后,提出一种基于最小学习参数神经网络的动态面轨迹控制方法。同时,分析了轨迹控制系统的闭环稳定性。最后,进行数值仿真的验证。结果表明,协作无人直升机能在0.5 s后迅速跟踪各自轨迹指令,并使跟踪轨迹的平均相对误差小于0.2%,从而实现精准协作搬运。本文提出的多无人直升机轨迹控制方法为进一步研究多无人机协同轨迹控制提供参考依据。

With the rapid development of multi-UAVs cooperative control technology, multi-UAVs suspension transport flight has become a hot research topic at home and abroad. In this paper, a dynamic surface trajectory control method based on neural network with minimum learning parameters is proposed for the cable-suspended swing problem and trajectory control problem of multi-unmanned helicopters cooperative suspension handling control. Firstly, the nonlinear dynamic model of multi-unmanned helicopters cooperative suspension transportation system is established.The unmeasurable cable tension, which will greatly impact the system dynamics and stability, is taken as the disturbance and estimated by the minimal-learning-parameter neural network estimator. Then, a dynamic surface trajectory control method based on neural network with minimum learning parameters is proposed. At the same time, the closed-loop stability of the trajectory control system is analyzed. Finally, the results of numerical simulation show that the cooperative unmanned helicopters can quickly track their trajectory instructions after 0.5 s, and make the average relative error of tracking trajectory less than 0.2%, so as to realize accurate cooperative handling. The trajectory control method of multi-unmanned helicopters proposed in this paper provides a reference basis for the further study of multi-UAVs cooperative trajectory control.