Robust and Optimal Trajectory Design of a Space Transportation System by Various Heuristic Optimization Methods

ABSTRACT This paper presents a robust optimization framework for Satellite Launch Vehicle (SLV) and upper stage trajectory design, integrating uncertainties to enhance flight performance, minimize steering workload, and improve reliability. Identifying a robust optimal trajectory under uncertainty is crucial. The methodology begins with developing a robust trajectory for a two-stage SLV. The upper stage trajectory was then optimized assuming the first stage remained fixed. Three-dimensional equations of motion served as constraints. Uncertainties (aerodynamic coefficients, dry mass, engine thrust) were modeled via mean and standard deviation. Four metaheuristic algorithms—genetic algorithm (GA), particle swarm optimization (PSO), grey wolf optimizer (GWO), and invasive weed optimization (IWO)—were utilized. Monte Carlo simulations with 300 iterations incorporated uncertainties. Results show significant trajectory accuracy improvements. For the upper stage, altitude error decreased by 77%, orbital velocity error by 68%, and flight path angle error by 90%. For the SLV, altitude error improved by 80%, orbital velocity error by 78%, and flight path angle error by 82%. These findings demonstrate the framework’s effectiveness in enhancing trajectory performance under uncertainty.