Robust adaptive backstepping neural networks control for spacecraft rendezvous and docking with input saturation. (May 2016) - Xia, Kewei; Huo, Wei
Abstract: This paper presents a robust adaptive neural networks control strategy for spacecraft rendezvous and docking with the coupled position and attitude dynamics under input saturation. Backstepping technique is applied to design a relative attitude controller and a relative position controller, respectively. The dynamics uncertainties are approximated by radial basis function neural networks (RBFNNs). A novel switching controller consists of an adaptive neural networks controller dominating in its active region combined with an extra robust controller to avoid invalidation of the RBFNNs destroying stability of the system outside the neural active region. An auxiliary signal is introduced to compensate the input saturation with anti-windup technique, and a command filter is employed to approximate derivative of the virtual control in the backstepping procedure. Globally uniformly ultimately bounded of the relative states is proved via Lyapunov theory. Simulation example demonstrates effectiveness of the proposed control scheme. Abstract : Highlights: We present a spacecraft rendezvous and docking control problem with coupled dynamics and uncertainties under input saturation. A novel switching controller is designed, which consists of a conventional adaptive neural controller dominating in the neural active region and an extra robust controller to pull back the transient outside the neural active region, so that the stability region is ensured to global. A command filter is introduced to estimate the derivative of a virtual control input in the backstepping procedure and two auxiliary signals are used to overcome control saturation via anti-wind up technique. Simulation example shows that the proposed control strategy has a good performance to accomplish the control objective.