Robust adaptive backstepping neural networks control for spacecraft rendezvous and docking with uncertainties

This paper investigates a robust adaptive backstepping neural networks control for spacecraft rendezvous and docking with the coupled position and attitude dynamics. Backstepping technique is applied as the main control structure. The uncertainties of the relative dynamics are compensated by using radial basis function neural networks (RBFNNs). An adaptive switching controller is designed by combining a conventional adaptive neural networks controller and an extra robust controller. The conventional RBFNNs dominate in the neural active region, while the robust controller retrieves the transient outside the active region. The controllers work together not only improving the control accuracy, but also reducing real-time computing burden of the controller. Lyapunov theory is employed to prove that the states are globally uniformly ultimately bounded. Simulation example is given to illustrate the effectiveness of the proposed control strategy.