  Obtaining models that can be used for control is of utmost importance to
ensure the guidance and navigation of spacecraft, like a Generic Parafoil
Return Vehicle (GPRV). In this paper, we convert a nonlinear model of the
atmospheric flight dynamics of an GPRV to a Linear Parameter-Varying (LPV)
description, such that the LPV model is suitable for navigation control design.
Automated conversion methods for nonlinear models can result in complex LPV
representation, which are not suitable for controller synthesis. We apply
several state-of-the-art techniques, including learning based approaches, to
optimize the complexity and conservatism of the LPV embedding for an GPRV. The
results show that we can obtain an LPV embedding that approximates the complex
nonlinear dynamics sufficiently well, where the balance between complexity,
conservatism and model performance is optimal.
