Neural-network-based backstepping control for the post-capture tethered space combination using HDO

In this paper, a novel neural-network-based backstepping control method is designed for the post-capture tethered space combination system subjected to multi-source disturbances and actuator saturation. To cope with the multi-source disturbances, the so-called HDO is put forward which is capable of addressing both modeled and unmodeled disturbances. These disturbances are not necessarily matched disturbances and hence the backstepping design procedure is utilized. In virtue of the radial basis function neural networks (RBFNNs), the unknown nonlinearities is estimated. Additionally, the anti-windup technique is employed to handle the actuator saturation phenomenon which is unavoidable in the post-capture tethered space combination system. Sufficient conditions are derived to guarantee that the post-capture tethered space combination system is stabilized while carrying out the non-cooperative target capture mission. Finally, a number of numerical simulations are conducted on the post-capture tethered space combination to validate the proposed methodology.