Robust Optimum Trajectory Design of a Satellite Launch Vehicle in the Presence of Uncertainties

Abstract

In this paper, a robust optimization method is developed to solve the Satellite Launch Vehicle (SLV) trajectory design problem in the presence of uncertainties. Given these uncertainties in the actual SLV ascent trajectory, it is important to find an optimal trajectory that is resistant to these uncertainties, as it results in increased flight performance, reduced steering-control system workload and increased SLV reliability. For this purpose, the optimization problem is first considered by applying to maximize the payload mass criterion as an objective function and three-dimensional equations of motions as the governing constraints. Then by adding mean and standard deviation parameters of uncertainties, the robust optimizer model is developed and the genetic algorithm is used to execute the model. Monte Carlo simulation is also used to analyze the results of uncertainties and its continuous feedback to the optimizer model. Finally, an optimal trajectory is obtained that is robust to the uncertainties effects such as aerodynamic coefficients, dry mass and thrust errors of the SLV. The results of the simulation show the validity of this claim.

Uncertainty quantification for multidisciplinary launch vehicle design using model order reduction and spectral methods

“…Two main approaches exist in the literature to propagate uncertainty in the multidisciplinary problem (Figure 1). The first one [8,11,39] is a nested loop method with Monte Carlo Simulation (MCS) at the outer level and optimal control at the inner level. Therefore, for each realization of the uncertain variables, an optimal control problem is solved resulting in a high computational cost.…”