robust design of a re-entry unmanned space vehicle by multi-fidelity evolution control
This paper addresses the preliminary robust design of a small-medium scale re-entry unmanned space vehicle. A hybrid optimisation 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 minimise the mean value of the maximum heat flux, the mean value of the maximum achievable distance, and 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 re-entry trajectory of each individual of the population. During the optimisation 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 multi-fidelity, evolution control approach.
unmanned space vehicles optimal control robust multi disciplinary design multi-objective evolutionary algorithms meta-modelling