摘要
介绍了基于熵的改进粒子群算法在再入高超声速滑翔飞行器轨迹快速优化问题中的应用。首先给出再入轨迹优化问题模型,选取三自由度的再入运动模型,性能指标为航程最远,约束条件包括过载、动压、热流密度、攻角等过程约束以及速度、高度等终端约束,控制变量为攻角。其次,设计了用于求解再入轨迹优化问题的改进粒子群算法,引入编码熵和系统熵的概念,在种群产生和进化的过程中,通过不断调整编码熵和系统熵的关系,防止种群陷入局部最优解。最后通过仿真对算法进行了验证。仿真结果表明算法对初值不敏感,并且能很好的满足攻防两端在轨迹优化时对算法时效性的要求。
An improved particle swarm optimization algorithm based on entropy is introduced to optimize trajectory of Reentry Hypersonic Glide Vehicle. Firstly, the model of reentry vehicle＇s trajectory optimization is introduced, and performance index maximizes the flying range. The flying process is constrained by overload, dynamic pressure, heating rate and angle of attack. The terminal state is confined by object location and velocity. The control variable is angle of attack. Then, the improved particle swarm optimization algorithm is designed. The code entropy and system entropy are defined. By adjusting the relation between code entropy and system entropy during initialization or evolving process, the algo- rithm could jump out of local optimal solution. Finally, the improved algorithm is validated by simula- tion. The simulation result shows that algorithm is insensitive to the guess and the algorithm timeliness meets the need of both offensive side and defensive side.
出处
《现代防御技术》
北大核心
2015年第6期74-80,共7页
Modern Defence Technology
关键词
熵
粒子群优化
高超声速滑翔飞行器
轨迹优化
再入
攻角
entropy
particle swarm optimization
hypersonic glide vehicle
trajectory optimization
reentry
angle of attack
