An adaptive backstepping sliding mode control method is adopted to solve the control problem of uncertain nonlinear systems with unknown actuator failures. A model for the nonlinear actuator is developed which includes hysteresis nonlinearity, partial loss of effectiveness and total loss of effectiveness. Radial basis function neural network is employed to approximate the unknown nonlinear functions, and the parameters of neural network are tuned on-line by adaptation laws to improve the effect of approximation. The dynamic surface control is combined with backstepping control to avoid the explosion of complexity in the traditional backstepping design method. The influence of modeling error and uncertain disturbances is suppressed by introducing the adaptive compensation term. The closed loop system is proved to be semi-globally uniformly ultimately bounded by theoretical analysis. Simulation results are presented to demonstrate the effectiveness of this method.