基于云模型的小卫星总体优化设计
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摘要
小卫星优化设计贯穿整个小卫星总体设计阶段，是小卫星研制重要组成部分，其设计优化的结果对小卫星初步设计和详细设计有着深远的影响，直接决定了卫星设计的质量和效率。针对小卫星任务设计的需求，建立合理的优化模型、设计高效的优化算法对提高小卫星优化设计的效率、缩短研制周期、降低研制成本都有着重大的作用。本文结合国防“十一五”预研课题和国家高技术研究发展计划(863)项目，对小卫星优化设计进行深入研究，重点解决符合任务需求的小卫星优化模型的建模以及设计高效的优化算法的问题，具体工作如下：
针对小卫星优化设计是一个包含连续变量和离散变量的混合变量优化问题，和求解空间是非凸的、不连通问题，结合太阳同步轨道对地观测小卫星观测任务的需求，建立了小卫星总体优化模型。该模型引入云模型(Cloud Model，CM)，并将其与进化算法相结合，设计了基于云模型的进化算法(Evolutionary AlgorithmBased on Cloud Model，EABCM)，对小卫星质量模型进行优化求解，在理论上对算法的收敛性给予了证明，在工程上采用数值仿真方法验证了EABCM优化算法的有效性。
针对小卫星成本优化设计的需求，在对比分析常见小卫星成本估算模型基础上，根据对地观测小卫星观测任务的需求，设计基于云模型的粒子群优化算法(Particle Swarm Optimization Base on Cloud Model，PSOBCM)，有效克服粒子群算法收敛速度慢、易陷入局部极值点的缺陷，并通过数值仿真验证了对于成本模型理论精度的分析以及PSOBCM优化算法的有效性。
针对小卫星可靠性要求较高的实际需求，首先分析了小卫星可靠性设计的概念、设计步骤以及可靠性估计方法；其次根据任务设计的需求，建立了小卫星可靠性和设计成本的多目标优化模型；为求解该多目标优化问题，借鉴多目标遗传算法的排序思想，设计了基于云模型的多目标优化算法(Multi-Objective EvolutionAlgorithm Based Cloud Model，MOEABCM)对其求解，并通过数值仿真结果验证了算法的有效性。
针对快速响应空间任务需求，首先分析了上面级航天器的总体设计思路和设计方法，并给出了上面级航天器各个分系统的设计步骤；然后依据上面级航天器总体优化设计的目标，分别构建上面级航天器总体设计的质量优化模型、成本优化模型和成本、可靠性的多目标优化模型；最后分别采用基于云模型的进化算法、基于云模型的粒子群算法和基于云模型的多目标进化算法来对三个总体设计优化模型分别求解，验证了基于云模型的优化算法的有效性和上面级航天器设计的合理性。
As an important part of the general design phase of small satellites, theoptimization design of small satellites runs throughout the whole general design process.The optimal results have a profound impact on the prelinary design and detailed designof small satellites, which directly determines the quality and efficiency of the satellitedesign. In order to meet the design needs of small satellites, establishing rationaloptimization models and designing efficient optimization algorithms have an importanteffect on improving the efficiency of small satellites design, shortening the developmentcycle and reducing the development cost. Supported by the Eleventh Five-years DefensePre-research topics and National High Technology Research and Development Program863, the optimization design of small satellites are studied deeply in this thesis whichmainly focuses on the problems about how to set up the optimization models meetingthe requirements of small satellites design and how to design efficient optimizationalgorithms. The main contents of this thesis are consisted of the following parts:
As the optimization design of small satellites is a mixed variables optimizationproblem containing both continuous varialbes and discrete variables and its solutionspace is often non-convex and not connected. With the observation missionrequirements of sun-synchronous earth observation small satellites, a optimizationmodel of small satellites quality is established. Combining evolutionary algorithms withCloud Model(CM), which is wide used in In the field of artificial intelligence, a newoptimization method named Evolutionary Algorithm Based on Cloud Model(EABCM)is proposed. The effectiveness of EABCM method is verified by the optimizationproblem of the small satellites quality.
In view of the requirements about the optimization design cost of small satellites,on the basis of analyzing and comparing. with the common cost models of smallsatellite, according to the requirements of earth observation mission, a new optimizationmethod named Particle Swarm Optimization Based on Cloud Model(PSOBCM) isproposed, which solve the defects for particle swarm optimization about convergingslowly and being easy to fall into local extreme point. By numerical simulation theanalysis results in theory about the accuracy of the cost models and the effectiveness ofthe proposed PSOBCM method areverified.
As for the higher requirements about reliability design of small satellites, firstly theconcepts and design steps of reliability design of small satellitesas well as the estimationmethod about the reliability are analyzed; secondly according to the needs of missiondesign, a multiobjective optimization model about the reliability and design cost of thesmall satellites is set up. To solve this multiobjective optimization problem, using thesorting idea of the multiobjective genetic algorithm, a new optimization method named Multi-Objective Evolution Algorithm Based Cloud Model(MOEABCM) is designed. Atlast the effectiveness of the MOEABCM method is verified by simulation results.
Given the requirements about the rapid response space mission, firstly the generaldesign ideas and methods of the upper-stage spacecraft is analyzed, and design steps ofthe subsystem for the upper-stage spacecraft is also shown; secondly according to thedesign objective of the upper-stage spacecraft, the quality optimization model, costoptimization model, cost and reliability multiobjective optimization model of theupper-stage spacecraft are respectively founded; at last using evolutionary algorithmbased on cloud model, particle swarm optimization based on cloud model andmulti-objective evolution algorithm based cloud model respectively, three generaloptimization design models are optimized. The effectiveness of the optimizationmethods based on cloud model and the rationality of the upper-stage spacecraft designare also verified.
针对小卫星优化设计是一个包含连续变量和离散变量的混合变量优化问题，和求解空间是非凸的、不连通问题，结合太阳同步轨道对地观测小卫星观测任务的需求，建立了小卫星总体优化模型。该模型引入云模型(Cloud Model，CM)，并将其与进化算法相结合，设计了基于云模型的进化算法(Evolutionary AlgorithmBased on Cloud Model，EABCM)，对小卫星质量模型进行优化求解，在理论上对算法的收敛性给予了证明，在工程上采用数值仿真方法验证了EABCM优化算法的有效性。
针对小卫星成本优化设计的需求，在对比分析常见小卫星成本估算模型基础上，根据对地观测小卫星观测任务的需求，设计基于云模型的粒子群优化算法(Particle Swarm Optimization Base on Cloud Model，PSOBCM)，有效克服粒子群算法收敛速度慢、易陷入局部极值点的缺陷，并通过数值仿真验证了对于成本模型理论精度的分析以及PSOBCM优化算法的有效性。
针对小卫星可靠性要求较高的实际需求，首先分析了小卫星可靠性设计的概念、设计步骤以及可靠性估计方法；其次根据任务设计的需求，建立了小卫星可靠性和设计成本的多目标优化模型；为求解该多目标优化问题，借鉴多目标遗传算法的排序思想，设计了基于云模型的多目标优化算法(Multi-Objective EvolutionAlgorithm Based Cloud Model，MOEABCM)对其求解，并通过数值仿真结果验证了算法的有效性。
针对快速响应空间任务需求，首先分析了上面级航天器的总体设计思路和设计方法，并给出了上面级航天器各个分系统的设计步骤；然后依据上面级航天器总体优化设计的目标，分别构建上面级航天器总体设计的质量优化模型、成本优化模型和成本、可靠性的多目标优化模型；最后分别采用基于云模型的进化算法、基于云模型的粒子群算法和基于云模型的多目标进化算法来对三个总体设计优化模型分别求解，验证了基于云模型的优化算法的有效性和上面级航天器设计的合理性。
As an important part of the general design phase of small satellites, theoptimization design of small satellites runs throughout the whole general design process.The optimal results have a profound impact on the prelinary design and detailed designof small satellites, which directly determines the quality and efficiency of the satellitedesign. In order to meet the design needs of small satellites, establishing rationaloptimization models and designing efficient optimization algorithms have an importanteffect on improving the efficiency of small satellites design, shortening the developmentcycle and reducing the development cost. Supported by the Eleventh Five-years DefensePre-research topics and National High Technology Research and Development Program863, the optimization design of small satellites are studied deeply in this thesis whichmainly focuses on the problems about how to set up the optimization models meetingthe requirements of small satellites design and how to design efficient optimizationalgorithms. The main contents of this thesis are consisted of the following parts:
As the optimization design of small satellites is a mixed variables optimizationproblem containing both continuous varialbes and discrete variables and its solutionspace is often non-convex and not connected. With the observation missionrequirements of sun-synchronous earth observation small satellites, a optimizationmodel of small satellites quality is established. Combining evolutionary algorithms withCloud Model(CM), which is wide used in In the field of artificial intelligence, a newoptimization method named Evolutionary Algorithm Based on Cloud Model(EABCM)is proposed. The effectiveness of EABCM method is verified by the optimizationproblem of the small satellites quality.
In view of the requirements about the optimization design cost of small satellites,on the basis of analyzing and comparing. with the common cost models of smallsatellite, according to the requirements of earth observation mission, a new optimizationmethod named Particle Swarm Optimization Based on Cloud Model(PSOBCM) isproposed, which solve the defects for particle swarm optimization about convergingslowly and being easy to fall into local extreme point. By numerical simulation theanalysis results in theory about the accuracy of the cost models and the effectiveness ofthe proposed PSOBCM method areverified.
As for the higher requirements about reliability design of small satellites, firstly theconcepts and design steps of reliability design of small satellitesas well as the estimationmethod about the reliability are analyzed; secondly according to the needs of missiondesign, a multiobjective optimization model about the reliability and design cost of thesmall satellites is set up. To solve this multiobjective optimization problem, using thesorting idea of the multiobjective genetic algorithm, a new optimization method named Multi-Objective Evolution Algorithm Based Cloud Model(MOEABCM) is designed. Atlast the effectiveness of the MOEABCM method is verified by simulation results.
Given the requirements about the rapid response space mission, firstly the generaldesign ideas and methods of the upper-stage spacecraft is analyzed, and design steps ofthe subsystem for the upper-stage spacecraft is also shown; secondly according to thedesign objective of the upper-stage spacecraft, the quality optimization model, costoptimization model, cost and reliability multiobjective optimization model of theupper-stage spacecraft are respectively founded; at last using evolutionary algorithmbased on cloud model, particle swarm optimization based on cloud model andmulti-objective evolution algorithm based cloud model respectively, three generaloptimization design models are optimized. The effectiveness of the optimizationmethods based on cloud model and the rationality of the upper-stage spacecraft designare also verified.
引文
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