当前位置: X-MOL 学术Isa Trans. › 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.)
A neural network based MRAC scheme with application to an autonomous nonlinear rotorcraft in the presence of input saturation
ISA Transactions ( IF 6.3 ) Pub Date : 2021-01-12 , DOI: 10.1016/j.isatra.2021.01.003
Yu Wang 1 , Aijun Li 1 , Shu Yang 1 , Qiang Li 2 , Zhao Ma 2
Affiliation  

This paper develops a neural-network-based model reference adaptive control (MRAC) scheme for a rotorcraft in the presence of input saturation. Such a control scheme provides acceptable tracking performance for the rotorcraft in a wide range of flight conditions. Combined with hyperbolic tangent functions, the MRAC scheme is capable of tracking the reference signals without violating input constraints. A modified projection operator is utilized to prevent system from parameter drift due to the strong nonlinearity and uncertainty of the rotorcraft mathematical model. Stability of the proposed MRAC scheme is proved based on Lyapunov stability theory. The performance of the resulting controller is tested by conducting numerical simulations for an autonomous rotorcraft in various flight missions.



中文翻译:

一种基于神经网络的 MRAC 方案,适用于存在输入饱和的自主非线性旋翼机

本文为存在输入饱和的旋翼机开发了一种基于神经网络的模型参考自适应控制 (MRAC) 方案。这种控制方案为旋翼机在广泛的飞行条件下提供了可接受的跟踪性能。结合双曲正切函数,MRAC 方案能够在不违反输入约束的情况下跟踪参考信号。由于旋翼飞行器数学模型的强非线性和不确定性,采用修正的投影算子来防止系统参数漂移。基于李雅普诺夫稳定性理论证明了所提出的 MRAC 方案的稳定性。通过在各种飞行任务中对自主旋翼机进行数值模拟来测试所得控制器的性能。

更新日期:2021-01-12
down
wechat
bug