| 引用本文: | 徐慧,蔡光斌,崔亚龙,侯明哲,姚二亮.高超声速滑翔飞行器再入轨迹优化[J].哈尔滨工业大学学报,2023,55(4):44.DOI:10.11918/202108083 |
| XU Hui,CAI Guangbin,CUI Yalong,HOU Mingzhe,YAO Erliang.Reentry trajectory optimization method of hypersonic glide vehicle[J].Journal of Harbin Institute of Technology,2023,55(4):44.DOI:10.11918/202108083 |
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| 高超声速滑翔飞行器再入轨迹优化 |
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徐慧1,蔡光斌1,崔亚龙2,侯明哲3,姚二亮1
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(1.火箭军工程大学 导弹工程学院,西安 710025; 2.96851部队,辽宁 盘锦,124202; 3.哈尔滨工业大学 航天学院,哈尔滨 150001)
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| 摘要: |
| 为提高高超声速滑翔飞行器再入轨迹优化问题求解速度和精度,提出了一种将改进的麻雀智能优化同参数化设计相结合的再入轨迹方法。首先,通过Tent混沌映射和精英反向种群方法初始化种群,利用黄金正弦策略进行种群的位置更新,并通过余弦策略减少侦察者数量,采用贪婪策略对种群的最优解进行选择和更新,在增强算法全局搜索能力的同时,不影响收敛速度。然后,将高超声速再入轨迹优化问题转化为攻角剖面和倾侧角剖面的参数化设计问题,将路径约束转化为阻力加速度再入飞行走廊,保证再入过程中始终满足路径约束,利用罚函数法处理终端约束,从而使得飞行器精确命中目标。最后,采用改进的麻雀智能优化算法对设计参数进行寻优,使得目标函数最优。仿真实验表明:本研究所提出的改进麻雀算法相较于原始麻雀算法、鲸鱼算法和粒子群算法收敛速度快,得到的高超声速滑翔飞行器再入轨迹精度有了进一步的提高；蒙特卡洛仿真实验说明,本研究所提出的高超声速滑翔飞行器再入轨迹优化算法具有一定的鲁棒性。 |
| 关键词: 高超声速滑翔飞行器 再入轨迹优化 改进麻雀算法 参数化设计 蒙特卡洛仿真 |
| DOI：10.11918/202108083 |
| 分类号:V448.235 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(7,6)；陕西省科技计划项目(2021JQ-327) |
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| Reentry trajectory optimization method of hypersonic glide vehicle |
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XU Hui1,CAI Guangbin1,CUI Yalong2,HOU Mingzhe3,YAO Erliang1
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（1.College of Missile Engineering, Rocket Force University of Engineering, Xian 710025, China; 2. No.96851 Troops, Panjin 124202, Liaoning, China; 3.School of Astronautics, Harbin Institute of Technology, Harbin, 150001）
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| Abstract: |
| To improve the speed and accuracy of the reentry trajectory optimization problem of the hypersonic glide vehicle, a reentry trajectory method combining improved sparrow intelligent optimization with parametric design is proposed. Firstly, the population is initialized by the Tent chaotic mapping and the elite reverse population method, and the position of the population is updated by the golden sine strategy. The number of scouts is reduced by the sine strategy, and the optimal solution for the population is selected and updated by the greedy strategy. The global search ability of the algorithm is enhanced without affecting the convergence rate. Then, the hypersonic reentry trajectory optimization problem is transformed into the parametric design problem of the attack angle profile and the bank angle profile, and the path constraint is transformed into the drag acceleration reentry flight corridor to ensure that the path constraint is always satisfied in the reentry process, and the penalty function method is used to ensure that the aircraft can accurately hit the target. Finally, the improved sparrow intelligent optimization algorithm is used to optimize the design parameters to make the objective function optimal. The simulation results show that the proposed improved sparrow algorithm has a faster convergence speed than the original sparrow algorithm, whale algorithm, and particle swarm algorithm, and the accuracy of the hypersonic glide vehicle reentry trajectory is further improved. The Monte Carlo simulation results show that the reentry trajectory optimization algorithm of the hypersonic glide vehicle proposed in this paper has certain robustness. |
| Key words: hypersonic glide vehicle reentry trajectory optimization improved sparrow algorithm parametric design Monte Carlo simulation |
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