Adaptive Back-stepping Neural Control for an Embedded and Tiltable V-tail Morphing Aircraft

This paper presents an adaptive back-stepping neural control (ABNC) method for the coupled nonlinear model of a novel type of embedded surface morphing aircraft. Based on a large number of aerodynamic data for different V-tail configurations, the longitudinal and lateral aerodynamic characteristics of the aircraft are analyzed, and a nonlinear model with six degrees-of-freedom is established. To avoid the problem of “differential explosion,” the controller is designed using the traditional back-stepping control (TBC) method with a first-order filter. Radial basis function neural networks are introduced to estimate the uncertainty and external disturbance of the model, and a controller based on the ABNC method is designed. The stability of the proposed ABNC controller is proved using Lyapunov theory, and it is shown that the tracking error of the closed-loop system converges uniformly within specified bounds. Simulation results show that the ABNC controller works well, with better tracking performance and robustness than the TBC controller.