title: Robust adaptive backstepping neural networks control for spacecraft rendezvous and docking with input saturation.
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.
authors.affiliation: The Seventh Research Division, Science and Technology on Aircraft Control Laboratory, Beihang University, Beijing 100191, PR China. Electronic address: kwxia@buaa.edu.cn.
authors.forename: Kewei
authors.initials: K
authors.lastname: Xia
authors.affiliation: The Seventh Research Division, Science and Technology on Aircraft Control Laboratory, Beihang University, Beijing 100191, PR China. Electronic address: weihuo@buaa.edu.cn.
authors.forename: Wei
authors.initials: W
authors.lastname: Huo
keywords: Adaptive neural networks; Command filter; Input saturation; Rendezvous and docking; Spacecraft control