Hardware-in-loop adaptive neural control for a tiltable V-tail morphing aircraft
Fuxiang Qiao, Jingping Shi, Xiaobo Qu, Yongxi Lyu
Abstract
This paper proposes an adaptive neural control (ANC) method for the coupled nonlinear model of a novel type of embedded surface morphing aircraft which has a tiltable V-tail. A nonlinear model with six-degrees-of-freedom is established. The first-order sliding mode differentiator (FSMD) is applied to the control scheme to avoid the problem of “differential explosion”. Radial basis function neural networks are introduced to estimate the uncertainty and external disturbance of the model, and an ANC controller is proposed based on this design idea. The stability of the proposed ANC controller is proved using Lyapunov theory, and the tracking error of the closed-loop system is semi-globally uniformly bounded. The effectiveness and robustness of the proposed method are verified by numerical simulations and hardware-in-the-loop (HIL) simulations.