GA multi-objective and experimental optimization for a tail-sitter small UAV

This paper introduces a Montecarlo Genetic Algorithm, hierarchical, multiobjective optimization of a Vertical Take Off landing Unmanned Aerial Vehicle having a tail sitter configuration. An optimization of the hierarchical type is introduced in place of the methods generally used multi-objective optimization, such as Pareto and “arbitrary” weighted sums. A Montecarlo method optimizes the weights of the final objective function used by the Genetic Algorithm. A very simple “spreadsheet based” algorithm defines the CAD model of the Genetic Algorithm individuals in order to evaluate the performance of the candidates. The optimization method described in this study appears to be very effective. Then experimental tests were conducted with scaled-down prototypes. Four flight tests were performed: Take Off, Cruise, Slow flight, Landing. A Taguchi matrix was defined for each experiment. The tests started from a prototype that comes directly from the Montecarlo Genetic Algorithm optimization and led to the final prototype shown along the paper (page 7, right figure). Unfortunately, the tail sitter approach proved poor control authority in the final phase of the vertical landing. Even the “final” prototype showed unsatisfactory behavior in case of erratic wind gusts. This unsolved problem is common to the tail sitter configuration that requires a power control by air jets or additional propeller to control the aircraft in the final phase of landing. Unfortunately, this necessity renders the tail sitter configuration inconvenient for small Unmanned Aerial Vehicles.