An integrated optimization of RLV reentry trajectory

The design and optimization of reentry trajectory is one of the most important tasks in the design and application of RLV. A well designed reentry trajectory can not only satisfy a variety of specified constraints during the atmospheric reentry and landing on a desired landing strip but also minimize the mass of the vehicle's thermal protection system. There are two primary methods which called "direct" and "indirect" in the trajectory optimization. When using "indirect" method, it is very difficult to guess the initial value of Lagrange multipliers because the optimization process is very sensitive to it. "Direct" method converts optimal control problems into parameter optimization problems thus it can amend the problem existing in the "indirect" method in a way. But when the trajectory dynamics are more nonlinear, such as in aerodynamic reentry problems, it is obligatory to construct a parameterization of the trajectory with a large number of variables and constraints. One will encounter the sparsity problem which affects the design of optimization algorithm in a number of significant ways. In this paper, the Genetic Algorithm is combined with a "direct" method called "Direct Collocation" and is used to solve the problem of RLV reentry trajectory optimization. Firstly, the models of the reentry trajectory optimization are established. Then, the discretization work is performed by dividing the vehicle trajectory into N segments and the vehicle states, vehicle controls at N + 1 nodes are treated as the optimal parameters. Finally, a floating point genetic algorithm is used to get the optimal solution and some simulation works are carried out. The simulation results indicate that the optimization method combining Genetic Algorithm with "Direct Collocation" works well during the reentry optimization.