A High Precision Adaptive Back-Stepping Control Method for Morphing Aircraft Based on RBFNN Method

一种基于RBFNN的变体飞机高精度自适应反步控制方法

School of Automation, Northwestern Polytechnical University, Xi'an 710072, China

Received: 29 August 2019

Abstract

To overcome the uncertainties of the nonlinear model of a morphing aircraft, this paper presents a high-precision adaptive back-stepping control method based on the radial basis function neural network (RBFNN). Firstly, based on the analysis of static and dynamic aerodynamic parameters of the morphing aircraft, its nonlinear control law is designed by using the conventional back-stepping method. The RBFNN is introduced to approximate online the uncertain terms of the nonlinear control law so as to improve its robustness. The robust term is designed to eliminate the approximation error caused by the RBFNN. Secondly, the tracking differentiator is designed through solving the virtual control variables, thus solving the "differential expansion" problem existing in the traditional back-stepping method. The Lyapunov stability analysis proves that our method can ensure that the tracking error of a closed-loop system converges finally and that its signals are uniformly bounded. Finally, the digital simulation model of the morphing aircraft is established with the MATLAB/Simulink; our method is compared with the conventional back-stepping control method. The simulation results show that our method has a higher control precision and stronger robustness.

摘要

针对变体飞机非线性模型的不确定性问题，提出了一种基于径向基神经网络（radial basis function neural networks，RBFNN）的高精度自适应反步控制方法。首先，在变体飞机静态和动态气动参数分析的基础上，运用传统反步法设计了非线性控制律，并引入径向基神经网络在线逼近系统的不确定项，提高系统鲁棒性；并设计鲁棒项消除径向基神经网络带来的逼近误差。其次，通过对虚拟控制变量进行求导项设计微分跟踪器，解决了传统反步法中存在的"微分膨胀"问题。通过Lyapunov稳定性分析，证明该方法能保证闭环系统跟踪误差最终收敛且一致有界。最后，基于Matlab/Simulink搭建了变体飞机的数字仿真模型，并与常规反步法进行了对比分析，仿真结果表明该方法具有控制精度高、鲁棒性强的特点。