电推进GEO卫星的改进粒子群轨道保持优化设计
Improved Particle Swarm Orbit-keeping Optimization Design for GEO Satellite with Electric Propulsion
吕跃勇 1王成 1李笑月 2郑重 3郭延宁1
作者信息
- 1. 哈尔滨工业大学航天学院, 哈尔滨 150001
- 2. 中国科学院微小卫星创新研究院, 上海 201203
- 3. 哈尔滨工业大学机电工程学院, 哈尔滨 150001
- 折叠
摘要
针对地球同步轨道(GEO)卫星轨道保持问题,提出了一种基于改进粒子群算法(PSO)的序列电推力轨道保持方法.首先,建立了GEO卫星高精度非线性轨道动力学模型和序列电推力模型.然后,设计了GEO卫星相对轨道保持策略,建立了以燃料消耗为性能指标的序列电推力轨道保持问题优化模型并进行了离散化.接着,通过引入差分进化算法和维度学习策略对粒子群优化算法进行了适应性改进,同时对推力大小和作用时间进行寻优计算.最后,通过数值仿真对所提出的改进粒子群优化算法进行了对比校验.结果表明,该方法在完成GEO卫星轨道保持任务的同时具备燃料消耗低和收敛速度快等优点.
Abstract
An improved particle swarm optimization(PSO)based sequential electric thrust orbit keeping method for geosynchronous orbit(GEO)satellites is proposed.Firstly,precise nonlinear orbit dynamics model and serial electric thrust model of GEO satellite are established.Next,the relative orbit keeping strategy of GEO satellites is designed,and the optimization model of the sequential electric thrust orbit keeping problem with fuel consumption as the performance index is established and discretized.Then,the differential evolution algorithm and dimension learning strategy are introduced to improve the PSO algorithm to optimize the thrust amplitude and action time simultaneously.Finally,the proposed improved PSO algorithm is compared and verified by numerical simulation.The results show that the proposed method has the advantages of low fuel consumption and fast convergence while completing the GEO satellite orbit maintenance task.
关键词
卫星轨道保持/电推进/粒子群优化/差分进化/维度学习Key words
Satellite orbit-keeping/Electric propulsion/Particle swarm optimization/Differential evolution/Dimensional learning引用本文复制引用
基金项目
空间智能控制技术实验室开放基金(HTKJ2022KL502012)
航天科技创新基金(SAST2023-085)
中国科学院青年创新促进会项目(2021293)
国家自然科学基金(U23B6001)
国家自然科学基金(62273118)
国家自然科学基金(12150008)
出版年
2024
NETL
NSTL
万方数据