Titolo della tesi: Quantum-inspired meta-heuristic Algorithm for Trajectory Optimization and Spacecraft Control
Meta-heuristic optimization methods have acquired a significant importance in obtaining good quality solutions to hard optimization problems, some of them, have been applied with success to spacecraft control and trajectory optimization. This dissertation considers a class of meta-heuristics which take inspiration from the principles of Quantum Mechanics, and studies the application of these methods to spaceflight optimization problems. In particular, the minimum energy state ground state) computation of a quantum system is considered for its analogies with the optimization problem; the property of the ground state of providing, in the right circumstances, a peak of probability in the global minimum of a potential function is particularly interesting. A ground state computation method, named Diffusion Monte Carlo, is adapted to create a meta-heuristic optimization algorithm based on sampling of the minimum energy state of a fictitious quantum system. The algorithm is then applied to several optimization problems, from minimization of simple bi-dimensional test functions, to increasingly difficult problems regarding interplanetary trajectories, attitude re-orientation maneuvers, and to devise a control strategy for trajectories subject to uncertainty and stochastic disturbances in a highly nonlinear dynamical environment. The algorithm performance is evaluated, also by comparison with other widely used meta-heuristic methods; the results prove that the Diffusion Monte Carlo algorithm is effective in dealing with the tested problems, consistently outperforming the comparison algorithms. In the final part, the discussion is expanded to a Quantum Computing framework, named Quantum Annealing, that, similarly to Diffusion Monte Carlo, deals with optimization problems by exploiting the ground state computation. The possibility of implementing a trajectory optimization problem on a Quantum Annealing machine is analyzed, and a suitable transcription method is given. Finally, a feasibility analysis is performed on whether a trajectory optimization problem could be solved by currently available quantum annealers.