Abstract: The characteristics of a folding-wing aerial vehicle undergo fairly great changes during its launching time. To fulfill the high robustness requirements of a flight control system, an adaptive robust nonlinear flight controller based on block backstepping is designed. A variable dynamic model is established, and the unknown uncertainty and disturbance caused by aerodynamic characteristic changes are adaptively approximated by radial basis function (RBF) neural networks. Dynamic surface control is employed to replace the differentiations of the virtual control law in traditional backstepping to overcome the problem of "term explosion". The closed-loop system is guaranteed to be bounded and the tracking errors are also proved to converge exponentially to a small neighborhood around zero by the Lyapunov approach. Furthermore,the effectiveness and robustness of the designed flight controller are verified by six degree-of-freedom (DOF) nonlinear flight simulations for the folding-wing aerial vehicle with unknown uncertainty.

Key words: folding-wing aerial vehicle, flight control, backstepping, RBF neural network, dynamic surface, unknown uncertainty