Nonlinear robust adaptive NN control for variable-sweep aircraft

Journal of Vibroengineering

Zongcheng Ma¹  An Liu¹  Jinfu Feng¹  Junhua Hu¹ 

[1] Aeronautics and Astronautics Engineering College, Air Force Engineering University,Xi’an, 710038, China;

关键词: minimal learning parameter;    dynamic surface control;    adaptive neural control;    variable-sweep aircraft;   

DOI  :  10.21595/jve.2017.18619

来源: DOAJ

【 摘 要 】

In this paper, we address the problem of altitude and velocity controllers design for variable-sweep aircraft with model uncertainties. The object is to maintain altitude and velocity during the wing transition process where mass distribution and aerodynamic parameters change significantly. Based on the functional decomposition, the longitudinal dynamics of the aircraft can be divided into altitude subsystem in non-affine pure feedback form and velocity subsystem. And then nonlinear robust adaptive NN velocity controller and altitude controller are designed with backstepping method to relax the prior requirements of aerodynamic parameters accuracy in linear LPV controller design. The method of filtered signal is used to circumvent the algebraic loop problem caused by the dynamics of non-affine pure feedback form. Dynamic surface control (DSC) and minimal learning parameters (MLP) techniques are employed to solve the problems of ‘explosion of complexity’ in the back-stepping method and the online updated parameters being too much. The robust terms have been introduced to eliminate the influences of approximation errors. According to the Lyapunov-LaSalle invariant set theorem, the semi-global boundedness and convergence of all the signals of the closed-loop system are proved. Simulation results are presented to illustrate the control algorithm with good performance.

【 授权许可】

Unknown