Robust Design of a Reentry Unmanned Space Vehicle by Multifidelity Evolution Control

This paper addresses the preliminary robust design of a small/medium-scale reentry unmanned space vehicle. A hybrid optimization technique is proposed that couples an evolutionary multi-objective algorithm with a direct-transcription method for optimal control problems. Uncertainties on the aerodynamic forces and vehicle mass are integrated in the design process, and the hybrid algorithm searches for geometries that 1) minimize the mean value of the maximum heat flux, 2) maximize the mean value of the maximum achievable distance, and 3) minimize the variance of the maximum heat flux. The evolutionary part handles the system-design parameters of the vehicle and the uncertain functions, while the direct-transcription method generates optimal control profiles for the reentry trajectory of each individual of the population. During the optimization process, artificial neural networks are used to approximate the aerodynamic forces required by the direct-transcription method. The artificial neural networks are trained and updated by means of a multifidelity, evolution-control approach.