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针对航天器多冲量最优变轨问题,建立了多冲量最优变轨的数值优化模型,给出了一种遗传算法(GA)与序列二次规划算法(SQP)结合的混合优化算法。该算法不需初值猜测,全局和局部搜索能力强并且计算效率高。仿真计算了燃料最优交会和时间最短拦截问题,比较了GA,SQP以及混合遗传算法的性能。针对混合遗传算法得到的不同冲量次数变轨的优化结果,分析了冲量次数对变轨性能指标的影响。结果表明混合遗传算法综合性能最高,冲量次数对不同性能指标的影响不同。仿真算例验证了模型和算法的有效性。
Aiming at the multi-impulse optimal orbit change problem of a spacecraft, a numerical optimization model of multi-impulse optimal orbit change is established and a hybrid optimization algorithm combining Genetic Algorithm (GA) and Sequential Quadratic Programming (SQP) is proposed. The algorithm does not require initial guess, global and local search ability and computational efficiency. The fuel optimal intersection and the shortest interception time were simulated and the performance of GA, SQP and hybrid genetic algorithm were compared. In allusion to the optimization results of the orbital change of different impulse numbers obtained by the hybrid genetic algorithm, the influence of the number of impulse on the performance index of the orbit change is analyzed. The results show that the hybrid genetic algorithm has the highest comprehensive performance and the impulse times have different effects on different performance indexes. The simulation example verifies the validity of the model and the algorithm.