基于改进DE-SQP算法的运载火箭轨迹优化方法研究  

Research on the optimization method of launch vehicle trajectory based on improved DE-SQP algorithm

作　　者：郭晶晶 王建华 于沫尧 项月 Guo Jingjing;Wang Jianhua;Yu Moyao;Xiang Yue(Space Engineering University,Beijing 101416,China;East China University of Science and Technology,Shanghai 200237,China)

机构地区：[1]航天工程大学,北京101416 [2]华东理工大学,上海200237

出　　处：《战术导弹技术》2025年第1期94-103,共10页Tactical Missile Technology

基　　金：国家自然科学基金(61903379);国防科技创新特区创新工作站项目(CX2023-04-01-01);航天工程大学科技创新培育基金。

摘　　要：针对多约束条件下运载火箭轨迹优化问题,提出一种融合改进差分进化算法和序列二次规划算法的轨迹DE-SQP优化方法。建立运载火箭质心运动模型和各项约束条件的数学表征模型;该创新设计改进差分进化算法生成初值,并利用序列二次规划方法快速局部寻优的组合优化策略。引入Chebyshev混沌映射,生成分布更为均匀且探索性更强的初始种群,同时融合反向学习策略,有效增加种群的多样性并加速收敛过程,利用改进差分进化算法生成优化轨迹的初值。基于序列二次规划方法显著的局部搜索能力,进一步在轨迹初值的基础上精准寻优,完成运载火箭轨迹的优化求解。数值仿真表明,改进的DE-SQP算法具有较强的全局优化和局部精确搜索能力,可以有效解决运载火箭轨迹优化问题,为相关理论研究和工程应用提供参考和技术支持。A DE-SQP optimization method for the trajectory of launch vehicles with multiple constraint conditions is proposed, which combines an improved Differential Evolution(DE) algorithm and a Sequential Quadratic Programming(SQP) algorithm. A mathematical representation model for the center of mass motion of launch vehicles and various constraint conditions is established. The innovative design improvement of differential evolution algorithm generates initial values and utilizes sequential quadratic programming methods for fast local optimization of combinatorial optimization strategy. The Chebyshev chaotic mapping is introduced to develop a more evenly distributed and exploratory initial population, and the opposition-based learning strategy is integrated to effectively increase population diversity and accelerate the convergence process, and an improved differential evolution algorithm is adopted to generate initial values for optimized trajectories.Based on the significant local search capability of the sequential quadratic programming method, further precise optimization is achieved on the initial trajectory, completing the optimization solution of the upward trajectory of the launch vehicles. Numerical simulations show that the improved DE-SQP algorithm has strong global optimization and local accurate search capabilities, and can effectively solve the trajectory optimization problem of launch vehicles.

关 键 词：差分进化算法 Chebyshev混沌映射 反向学习 DE-SQP组合优化 伪谱法 轨迹优化 

分 类 号：V421.1[航空宇航科学与技术—飞行器设计]