当前位置: X-MOL 学术J. Franklin Inst. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Robust adaptive neural network-based compensation control of a class of quadrotor aircrafts
Journal of the Franklin Institute ( IF 4.1 ) Pub Date : 2020-09-19 , DOI: 10.1016/j.jfranklin.2020.09.009
Xiao-Zheng Jin , Tao He , Xiao-Ming Wu , Hai Wang , Jing Chi

In this paper, the position and attitude trajectory tracking problem of a class of quadrotor aircrafts with bounded external disturbances and state-dependent internal uncertainties is addressed. Neural network (NN)-based methods are adopted to approximate the unknown uncertainties, while adaptive technique is used to estimate the unknown bounds of disturbances. Then, an adaptive compensation control scheme based on neural networks is proposed to compensate for the effects of disturbances and uncertainties. On the basis of Lyapunov stability theorem, bounded trajectory tracking of a position subsystem and asymptotic trajectory tracking of an attitude subsystem can be achieved by using the NN-based adaptive compensation control scheme in the presence of internal uncertainties and external disturbances. A numerical simulation is carried out to verify the effectiveness of the designed control method of quadrotor aircrafts.



中文翻译:

一类四旋翼飞机的鲁棒自适应神经网络补偿控制

本文针对一类具有受限外部扰动和状态相关内部不确定性的四旋翼飞机的位置和姿态轨迹跟踪问题进行了研究。采用基于神经网络(NN)的方法来近似未知不确定性,而采用自适应技术来估计未知的扰动范围。然后,提出了一种基于神经网络的自适应补偿控制方案,以补偿干扰和不确定性的影响。基于李雅普诺夫稳定定理,在存在内部不确定性和外部干扰的情况下,通过使用基于神经网络的自适应补偿控制方案,可以实现位置子系统的有界轨迹跟踪和姿态子系统的渐近轨迹跟踪。

更新日期:2020-11-06
down
wechat
bug