| 基于改进粒子群算法的再入飞行器轨迹优化 |
| |
| 引用本文： | 苏茂,王永骥,刘磊,常松涛.基于改进粒子群算法的再入飞行器轨迹优化[J].湖南农业大学学报(自然科学版),2011(4):55-59. |
| |
| 作者姓名： | 苏茂 王永骥 刘磊 常松涛 |
| |
| 作者单位： | （华中科技大学 控制科学与工程系 图像信息处理与智能控制教育部重点实验室,湖北 武汉430074） |
| |
| 摘 要： | 采用基于距离量度和自适应惩罚相结合的约束处理技术的改进粒子群优化算法（PSO）应用于再入飞行器轨迹优化，避免适应值函数中复杂的罚函数及罚因子的设计，提高优化算法的通用性。以高超声速飞行器最小控制量再入轨迹优化为例，并对飞行器运动模型进行简化及控制量参数化。对两种不同的高超声速飞行器模型进行优化，仿真结果验证算法的有效性及通用性。
|
| 关 键 词： | 再入飞行器 轨迹优化 粒子群优化算法 多约束处理 |
Design of Reentry Vehicle Trajectory Optimization Based on Improved Particle Swarm Optimization Algorithm |
| |
| SU Mao,WANG Yong-ji,LIU Lei,CHANG Song-tao.Design of Reentry Vehicle Trajectory Optimization Based on Improved Particle Swarm Optimization Algorithm[J].Journal of Hunan Agricultural University,2011(4):55-59. |
| |
| Authors: | SU Mao WANG Yong-ji LIU Lei CHANG Song-tao |
| |
| Institution: | (Department of Control Science and Engineering, Huazhong University of Science and Technology, Key Laboratory of Image Processing and Intelligent Control, Wuhan 430074,China) |
| |
| Abstract: | A method using Partical Swarm Optimization(PSO) is applied to reentry vehicle trajectory optimization, the method based on distance measures and adaptive penalty functions avoids the complex design for penalty functions and penalty factors, and develops the generality of the algorithm. For the minimum control energy reentry trajectory optimization for hypersonic glide vehicles, model is simplified, control variable is parameterized. Two different hypersonic vehicles are optimized by the method, The effectiveness and generality of the method are demonstrated by simulation results. |
| |
| Keywords: | reentry vehicle trajectory optimization partical swarm optimization multi-constrains handling |
|
| 点击此处可从《湖南农业大学学报(自然科学版)》浏览原始摘要信息 |
| 点击此处可从《湖南农业大学学报(自然科学版)》下载免费的PDF全文 |
