Adaptive Neural Network Dynamic Surface Control of the Post-capture Tethered Spacecraft

This paper presents an adaptive neural network dynamic surface control approach for the post-capture tethered spacecraft, where model uncertainties, input saturation, and state constraints exist. First, a dynamic model of the post-capture tethered spacecraft considering the three-dimensional attitude of the target satellite is derived by the Lagrange formalism. Then, the neural network is adopted to compensate the model uncertainties and the effects of input saturation, and a barrier Lyapunov function is employed to prevent the violation of the state constraints. The asymptotic stability of the closed-loop system is guaranteed by the Lyapunov stability theory. Finally, simulation results are given to illustrate the effectiveness of the proposed controller.