Robust Adaptive Back-stepping Control Design Based on RBFNN for Morphing Aircraft

This paper presents a robust adaptive back-stepping control base on radial basis function neural networks (RBFNN) for the nonlinear morphing aircraft. The adaptive term constructed by the RBFNN approximate the uncertainties of the system, and the robust term is designed to eliminate approximation error between the real value and evaluated value approximated by RBFNN. The performance and stability of controller are guaranteed by these two terms. It is proved by means of Lyapunov theory that the track error can be convergent and the signals are uniformly bounded. Simulation results show that the proposed controller can ensure good tracing performance of the morphing aircraft and suppress uncertainties of the system effectively.