Differential Evolution Based Ascent Phase Trajectory Optimization for a Hypersonic Vehicle

In this paper, a new method for the numerical computation of optimal, or nearly optimal, solutions to aerospace trajectory problems is presented. Differential Evolution (DE), a powerful stochastic real-parameter optimization algorithm is used to optimize the ascent phase of a hypersonic vehicle. The vehicle has to undergo large changes in altitude and associated aerodynamic conditions. As a result, its aerodynamic characteristics, as well as its propulsion parameters, undergo drastic changes. Such trajectory optimization problems can be solved by converting it to a non-linear programming (NLP) problem. One of the issues in the NLP method is that it requires a fairly large number of grid points to arrive at an optimal solution. Differential Evolution based algorithm, proposed in this paper, is shown to perform equally well with lesser number of grid points. This is supported by extensive simulation results.