Neural network approximation-based backstepping sliding mode control for spacecraft with input saturation and dynamics uncertainty

Abstract

This paper proposed a neural network approximation-based backstepping sliding mode control approach (NN-BSMC) to address the problem of attitude tracking control for spacecraft in the presence of inertial uncertainties, external disturbances and input saturation. First, the attitude dynamics model of the spacecraft is presented, and the error dynamics model with input saturation is derived. Second, a control scheme using sliding mode control (SMC) and backstepping technique is proposed to guarantee the robustness against the dynamics uncertainty and deal with the effect of input saturation. Under this framework, a neural network (NN) approximator is developed to online estimate the dynamics uncertainty of the spacecraft. Adaptive laws are designed to update the NN weight and estimate the unknown bound of approximation error. The control law does not require the prior knowledge about the bounds on the uncertainty and can provide complete compensation for the uncertainty. Moreover, a Lyapunov-based approach is employed to prove the global stability of the closed-loop system and the asymptotical convergence of the attitude tracking errors. Finally, numerical simulations are carried out to demonstrate the effectiveness and robustness of the proposed controllers. Contrasting simulation results indicate that the neural network backstepping sliding mode controller reduces the chattering effectively and has better performance against the sliding mode controller.