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Differential Evolution Based Ascent Phase Trajectory Optimization for a Hypersonic Vehicle

机译：基于差分进化的超音速飞行器上升相轨迹优化

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摘要

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.
机译：本文提出了一种新的数值计算方法，用于对航空航天轨迹问题的最优解或接近最优解进行数值计算。差分进化（DE）是一种功能强大的随机实参数优化算法，用于优化超音速飞行器的上升阶段。车辆必须经历高度变化和相关的空气动力学条件。结果，其空气动力学特性及其推进参数发生了巨大变化。这种轨迹优化问题可以通过将其转换为非线性规划（NLP）问题来解决。 NLP方法的问题之一是，它需要相当数量的网格点才能获得最佳解决方案。本文提出的基于差分进化的算法被证明在较少的网格点数下同样表现良好。大量的仿真结果对此提供了支持。

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