Abstract:

An adaptive Backstepping sliding mode control method is proposed for a class of uncertain nonlinear systems with input constraints. A model for the nonlinear actuator is developed, which includes input constrained situations such as dead zone, backlash, saturation, hysteresis, and unknown faults such as partial loss of effectiveness fault and actuator stuck fault. Radial basis function neural network is employed to approximate the unknown nonlinear functions. The explosion of complexity is avoided in the traditional Backstepping design method by introducing a first order filter. Adaptive approximate variable structure control is effective to reduce the chatting of the control signal. Theoretical analysis and simulation results are presented to demonstrate the effectiveness of this method by adaptively adjusting control input.