Ascent trajectory optimisation for a single-stage-to-orbit vehicle with hybrid propulsion

Tools

Pescetelli, Fabrizio and Minisci, Edmondo and Maddock, Christie and Taylor, Ian and Brown, Richard (2012) Ascent trajectory optimisation for a single-stage-to-orbit vehicle with hybrid propulsion. In: 18th AIAA/3AF International Space Planes and Hypersonic Systems and Technologies Conference, 2012-09-24 - 2012-09-28.

[thumbnail of Minisci_E_et_al_Pure_Ascent_trajectory_optimisation_for_a_single_stage_to_orbit_vehicle_with_hybrid_propulsion.pdf]
Preview
 PDF. Filename: Minisci_E_et_al_Pure_Ascent_trajectory_optimisation_for_a_single_stage_to_orbit_vehicle_with_hybrid_propulsion.pdf
Preprint
Download (2MB)| Preview

Abstract

This paper addresses the design of ascent trajectories for a hybrid-engine, high performance, unmanned, single-stage-to-orbit vehicle for payload deployment into low Earth orbit. A hybrid optimisation technique that couples a population-based, stochastic algorithm with a deterministic, gradient-based technique is used to maximize the nal vehicle mass in low Earth orbit after accounting for operational constraints on the dynamic pressure, Mach number and maximum axial and normal accelerations. The control search space is first explored by the population-based algorithm, which uses a single shooting method to evaluate the performance of candidate solutions. The resultant optimal control law and corresponding trajectory are then further refined by a direct collocation method based on finite elements in time. Two distinct operational phases, one using an air-breathing propulsion mode and the second using rocket propulsion, are considered. The presence of uncertainties in the atmospheric and vehicle aerodynamic models are considered in order to quantify their effect on the performance of the vehicle. Firstly, the deterministic optimal control law is re-integrated after introducing uncertainties into the models. The proximity of the final solutions to the target states are analysed statistically. A second analysis is then performed, aimed at determining the best performance of the vehicle when these uncertainties are included directly in the optimisation. The statistical analysis of the results obtained are summarized by an expectancy curve which represents the probable vehicle performance as a function of the uncertain system parameters. This analysis can be used during the preliminary phase of design to yield valuable insights into the robustness of the performance of the vehicle to uncertainties in the specification of its parameters.

ORCID iDs

Pescetelli, Fabrizio ORCID logoORCID: https://orcid.org/0000-0002-4672-2039, Minisci, Edmondo ORCID logoORCID: https://orcid.org/0000-0001-9951-8528, Maddock, Christie ORCID logoORCID: https://orcid.org/0000-0003-1079-4863, Taylor, Ian ORCID logoORCID: https://orcid.org/0000-0002-0082-5542 and Brown, Richard ORCID logoORCID: https://orcid.org/0000-0003-2754-5871;
CORE (COnnecting REpositories)