Abstract
The paper presents the Particle Swarm Optimization (PSO) technique as a possible approach to identify the globally optimal guidance history and configuration deployment sequence within different flight regimes an atmospheric entry-descent-landing (EDL) vehicle with a variable architecture has to deal with. The aerodynamics data set is dynamically generated according to the configuration solution identified by the current optimization iteration. The flight history is split according to the flight regime experienced by the vehicle in parallel Particle Swarm Optimization architectures to better exploit and visit the wide solution space. A 3D dynamics is modelled to generate the differential constraints the optimization must answer to. The optimization criteria vector is focused on multidisciplinary aspects such as the heat load experienced together with the precision landing conditions. Simulations on Mars atmosphere entry revealed the efficiency of the optimization architecture to detect the related Pareto front.