Fault self-repair strategy based on evolvable hardware and reparation balance technology

In the face of harsh natural environment applications such as earth-orbiting and deep space satellites, underwater sea vehicles, strong electromagnetic interference and temperature stress,the circuits faults appear easily. Circuit faults will inevitably lead to serious losses of availability or impeded mission success without self-repair over the mission duration. Traditional fault-repair methods based on redundant fault-tolerant technique are straightforward to implement, yet their area, power and weight cost can be excessive. Moreover they utilize all plug-in or component level circuits to realize redundant backup, such that their applicability is limited. Hence, a novel selfrepair technology based on evolvable hardware（EHW） and reparation balance technology（RBT） is proposed. Its cost is low, and fault self-repair of various circuits and devices can be realized through dynamic configuration. Making full use of the fault signals, correcting circuit can be found through EHW technique to realize the balance and compensation of the fault output-signals. In this paper, the self-repair model was analyzed which based on EHW and RBT technique, the specific self-repair strategy was studied, the corresponding self-repair circuit fault system was designed, and the typical faults were simulated and analyzed which combined with the actual electronic devices. Simulation results demonstrated that the proposed fault self-repair strategy was feasible. Compared to traditional techniques, fault self-repair based on EHW consumes fewer hardware resources, and the scope of fault self-repair was expanded significantly.     

太阳能飞机翼肋结构拓扑优化设计

太阳能飞机翼肋结构轻薄且数量众多,其结构形式的合理性对于结构减重、机翼刚度设计等具有重要意义。基于现有太阳能飞机翼肋的结构形式,提出一种翼肋拓扑优化设计方法:首先,应用双向渐进结构优化法(BESO),以单元应力为判断指标,对太阳能飞机典型翼肋进行结构拓扑优化;其次,基于拓扑优化结果对翼肋进行结构细节设计;最后,通过有限元计算,验证翼肋的结构强度、刚度和静稳定性。通过本文方法,能够得到太阳能飞机翼肋更为合理的结构布局。     

基于及时修正策略的成败型产品可靠性增长分析

针对及时修正试验策略，根据Fries可靠性增长模型和Duane学习曲线特性，基于不同的失败类型对模型进行了扩展。用进化算法对似然函数求极值，获得了参数极大似然估计，以及最终研制阶段可靠性点估计。对某固体火箭发动机可靠性增长实例的分析结果表明，与经典评估方法相比，本文模型更具优越性。     

基于进化神经网络的组合导航系统滤波算法研究

组合导航系统中,卡尔曼滤波算法的估计特性依赖于精确的系统模型和准确的外观测数据.任何一个条件不满足将导致滤波精度下降甚至发散.为此,本文引入进化神经网络和自适应卡尔曼滤波技术.仿真结果证明,文中所提算法能够有效克服常规卡尔曼滤波的缺点,并保持较高的精度.     

Automated design architectures for co-orbiting spacecraft swarms for planetary moon mapping

This work describes the design and optimization of spacecraft swarm missions to meet spatial and temporal visual mapping requirements of missions to planetary moons, using resonant co-orbits. The algorithms described here are a part of Integrated Design Engineering and Automation of Swarms (IDEAS), a spacecraft swarm mission design software that automates the design trajectories, swarm, and spacecraft behaviors in the mission. In the current work, we focus on the swarm design and optimization features of IDEAS, while showing the interaction between the different design modules. In the design segment, we consider the coverage requirements of two general planetary moon mapping missions: global surface mapping and region of interest observation. The configuration of the swarm co-orbits for the two missions is described, where the participating spacecraft have resonant encounters with the moon on their orbital apoapsis. We relate the swarm design to trajectory design through the orbit insertion maneuver performed on the interplanetary trajectory using aero-braking. We then present algorithms to model visual coverage, and collision avoidance in the swarm. To demonstrate the interaction between different design modules, we relate the trajectory and swarm to spacecraft design through fuel mass, and mission cost estimations using preliminary models. In the optimization segment, we formulate the trajectory and swarm design optimizations for the two missions as Mixed Integer Nonlinear Programming (MINLP) problems. In the current work, we use Genetic Algorithm as the primary optimization solver. However, we also use the Particle Swarm Optimizer to compare the optimizer performance. Finally, the algorithms described here are demonstrated through numerical case studies, where the two visual mapping missions are designed to explore the Martian moon Deimos.     

进化算法在系统可靠性多目标优化中的应用

为解决复杂系统可靠性设计的多目标优化问题，采用NSGA-Ⅱ进化算法，在一次性获得大量均匀分布的非劣解后，根据定义的满意度函数对每个非劣解进行模糊评价。运用群体排序、小生境、比较操作和精英保留等关键技术，将整个非劣解集分为若干个具有不同性能特征的类，决策者可根据各自的偏好从中选择最终的满意解。分析结果表明，进化算法具有较高的优化效率。     

坚持科学发展观推进思想政治理论课教学改革

思想政治理论课教学，必须坚持科学发展观，以学生为本，充分发挥学生的主体作用，注重学生的自我教育和能力的提高，一切为了学生的成长成才，一切着眼于调动和依靠学生内在的积极性，使学生德、智、体、美全面协调发展。     

一个新的研究领域——演化硬件

演化硬件的概念最初是由HugodeGaris于1992年提出的。硬件演化的目的在于使硬件系统能够像生物一样改变其自身的结构以适应环境的变化。本文首先给出演化硬件的定义,继而介绍演化硬件的物质基础──可编程专用集成电路和演化硬件的理论基础──演化算法。进而研究实现硬件演化的方法,最后讨论演化硬件的发展前景。     

基于进化算法的区域导航(RNAV)航路规划研究

本文采用进化算法,结合进化算法与区域导航航路规划的特点,提出了基于进化算法的区域导航航路规划问题。通过建立航路规划基本框架,进行航路编码、约束条件限制、初始种群设置等工作,然后依据选择机制建立新的航路链。     

基于进化策略的月球软着陆探测器轨道设计

应用进化策略和微分修正法建立了一套多约束、多目标条件下的月球软着陆轨道设计方法. 根据中国发射场和火箭运载的实际情况, 给出了软着陆轨道需要满足的过程约束及终端条件, 提出了利用进化策略进行轨道初步设计,通过微分修正法对初步设计结果进行修正的软着陆轨道设计思路, 并采用STK进行了仿真和结果验算. 分析表明, 基于进化策略的初步设计能够为微分修正提供良好的初值, 保证了其收敛性. STK仿真结果验证了设计思路的有效性及结果的正确性. 本文提出的方法能够为月球软着陆轨道设计提供参考.