Optimization design of chiral hexagonal honeycombs with prescribed elastic properties under large deformation

Flexible chiral honeycomb cores generally exhibit nonlinear elastic properties due to large geometric deformation. The effective elastic moduli and Poisson’s ratio typically vary with an increase in deformation. Here, the size and shape optimization of the chiral hexagonal honeycombs was performed to keep the Young’s moduli and Poisson's ratio unchanged under large deformations. The size of the honeycomb unit cell and the position coordinates of the key points were defined simultaneously as design variables. The equivalent Young's modulus and Poisson’s ratio of chiral honeycombs were calculated through geometric nonlinear analysis. The objective was to minimize the allowable tolerance between the prescribed and actual properties within the range of the target strain. A genetic algorithm was then adopted. The optimal results demonstrate that the chiral honeycombs can maintain effective elastic properties that do not vary under large deformation. These results are meaningful to morphing aircraft designs.     

基于角动量守恒的空间机器人动力学参数辩识

空间机器人由于加工、装配误差以及在轨燃料消耗等因素使其名义公称参数与实际 动力学参数相比存在一定的误差。空间机器人路径规划和地面机器人不同,其广义雅克比 矩阵包含动力学参数,使计算的轨迹偏离实际要求的路径,引起末端位姿误差。本文根 据自由飘浮空间机器人的角动量守恒方程式,对一种两自由度空间机器人的基座以及机械臂 的各个关节分别单独做三次多项式轨迹激励,利用基于偏差模型的最小二乘法和遗传算 法对动力学参数进行辨识。仿真结果表明,遗传算法的计算稳定性和对动力学参数辩识精 度优于最小二乘法。〖JP〗     

带机械臂的月面巡视探测器的路径规划方法研究

针对巡视探测器路径规划和机械臂路径规划的不同和两者需要依次执行的连接需求,文章将月面巡视探测器的路径规划过程分为三个阶段：巡视探测器的路径规划、机械臂的路径规划、器臂动态联合的路径规划,针对不同的规划分别提出了不同的规划方法,并进行了仿真验证。研究结果表明,巡视探测器的整个路径规划是一个复杂的运算规划过程,在非结构化月面环境下,基于月面三维数字高程图采用改进的启发式搜索（A＊）算法,可以比较高效地完成巡视探测器的路径规划;在采用蒙特卡罗法建立机械臂可达工作空间的基础上,可以比较简单、准确地获取机械臂的规划路径;巡视探测器整个就位探测过程的实现需要两种规划的动态联合。     

基于CNS定位误差的平行航路间隔安全评估

主要对平行航路中基于CNS定位误差的飞行间隔安全评估问题进行了研究。首先分析了CNS定位误差对飞机碰撞风险的影响。其次运用概率论方法得到了CNS定位误差下飞机纵向、侧向和垂直三个方向上的碰撞风险计算模型,并根据模型利用牛顿迭代算法,通过给定某一方向安全目标水平计算CNS定位误差下相应的安全间隔。最后对平行航路三个方向的飞行间隔进行了评估计算,将结果与实际相比较,表明该模型可行。     

区间不确定性下的空中作战行动过程优选方法

针对空中作战行动过程（COA）设计问题,根据动态影响网（DINs）理论和改进快速非支配排序遗传（NSGA-Ⅱ）算法,提出一种基于DINs和区间多目标优化的空中作战过程优选方法。首先,分析空中作战过程基本概念,分别进行静态和动态建模,并对参数不确定性进行分析。然后,基于改进Kendall协和系数检验法确定一致性检验后的关键参数,设计DINs概率传播算法。随后,分析期望效果实现概率与各关键参数的相关关系,在分析行动过程优选效果评价指标基础上,采用改进NSGA-Ⅱ算法对模型进行求解。最后,通过多组仿真案例,验证了模型的合理性,以及算法的有效性和优越性。     

Constant thrust fuel-optimal control for spacecraft rendezvous

In this paper, constant thrust rendezvous is studied and the optimal rendezvous time is calculated by using continuous genetic algorithm. Firstly, the relative position parameters of the target spacecraft are obtained by using the vision measurement and the target maneuver positions are calculated through the isochronous interpolation method. Then, the results of the calculation of constant thrust rendezvous is founded by processing with multivariate linear regression method. Next, a new switching control law is designed based on the thrust acceleration sequence and the on time of thrusters which can be computed by the time series analysis method. The perturbations and fuel consumptions are addressed during the computation of the on time of thrusters.     

大型磁悬浮CMG转子的组合优化策略

为提高空间站用大型磁悬浮CMG（Control Moment Gyroscope）系统的设计效率,并获得最优设计参数,针对高转速（12000r/min）大角动量（1000N·m·s）磁悬浮转子进行优化设计与分析。提出了一种基于遗传算法(Genetic Algorithm, GA)和序列二次规划算法(Sequential Quadratic Programming, NLPQL)的组合优化策略来优化转子组件。采用ANSYS进行参数化建模,通过ISIGHT软件集成ANSYS实现优化过程。取转子质量和最大等效应力分别最小为优化目标,选择转子轮盘结构尺寸为设计变量,并根据转子运行工况对转子提出包括尺寸、强度、效能等方面约束限制。与GA、NLPQL算法相比组合优化策略的优化结果要优于单纯使用一种全局优化算法或局部优化算法。组合优化策略结果表明转子质量减少了3.92%;安全系数提高了2.69%。实验结果证明了组合优化策略的设计结果。     

基于遗传算法的重定位容错方法的研究

随着网络成为人们获取信息的主要工具,人们对网络可靠性的要求也越来越高。针对网络中的结点出现故障而影响网络传输效率和可靠性问题,进行了基于遗传算法的重定位容错方法的研究。首先,描述了树型网络结构,根据网络组件的开销提出了网络代价目标函数。其次,阐述了遗传算法最小代价路径的搜索过程,提出了基于遗传算法的重定位容错方法。最后,通过仿真实验验证了重定位容错方法在路径搜索中的有效性。这样既缩短了路径搜索时间,又提高了网络传输信息的可靠性。     

基于组合优化算法的临近空间飞行器轨迹优化

提出一种临近空间飞行器轨迹优化方法,利用基于支持向量机与遗传算法的组合优化算法,解决多约束条件下的高效轨迹优化问题。首先,建立临近空间飞行器轨迹优化数学模型。然后,通过参数化方法和惩罚函数法将轨迹优化问题转化为约束参数优化问题。在此基础上,提出一种求解无约束参数优化问题的组合优化算法,通过支持向量机对遗传过程中产生的种群进行分类,提高基本遗传算法的计算效率,结合轨迹优化数学模型,给出轨迹优化算法。最后,以临近空间飞行器航程最远轨迹优化问题为例,进行数学仿真分析。仿真结果表明,针对给定的算例,文中提出的方法与基于基本遗传算法的轨迹优化方法相比,计算效率显著提高。     

Orbit Design for Responsive Space Using Multiple-objective Evolutionary Computation

Responsive orbits have exhibited advantages in emergencies for their excellent responsiveness and coverage to targets. Generally, there are several conflicting metrics to trade in the orbit design for responsive space. A special multiple-objective genetic algorithm, namely the Nondominated Sorting Genetic Algorithm Ⅱ (NSGAⅡ), is used to design responsive orbits. This algorithm has considered the conflicting metrics of orbits to achieve the optimal solution, including the orbital elements and launch programs of responsive vehicles. Low-Earth fast access orbits and low-Earth repeat coverage orbits, two subtypes of responsive orbits, can be designed using NSGAⅡ under given metric tradeoffs, number of vehicles, and launch mode. By selecting the optimal solution from the obtained Pareto fronts, a designer can process the metric tradeoffs conveniently in orbit design. Recurring to the flexibility of the algorithm, the NSGAⅡ promotes the responsive orbit design further.