Comparison of Deterministic and Stochastic Global Optimization Methods for Real-Time Generation of Guidance Trajectories

Guidance algorithms for the return flight of suborbital spaceplanes must generate a variety of guidance trajectories that satisfy terminal conditions even in unexpected abort operations. To tackle this issue, a trajectory optimization method that combines convex quadratic programming and a global derivative-free optimization technique in a nested structure has been recently studied by the authors. This hybrid method efficiently explores the three-dimensional Bezier trajectories and associated guidance commands that exactly fulfill the equality terminal conditions and command continuity. In this paper, Monte-Carlo simulations are performed to investigate the applicability of this guidance method to the realistic scenario of unpowered return flight. Six stochastic evolutionary algorithms, a Bayesian optimization method, and three deterministic search algorithms are implemented and tested as global optimizers. They are compared in terms of computational and implementational complexities, robustness, and diversity of solutions obtained. The results show that reliable and real-time trajectory generation is possible, when an optimizer and its settings are properly chosen. It also reveals that diverse trajectories between initial and terminal conditions are successfully generated.