Reliability design optimization of composite structures based on PSO together with FEA

The present work aims to develop a method for reliability-based optimum design of composite structures. A procedure combining particle swarm optimization (PSO) and finite element analysis (FEA) has been proposed. Numerical examples for the reliability design optimization (RDO) of a laminate and a composite cylindrical shell are worked out to demonstrate the effectiveness of the method. Then a design for composite pressure vessels is studied. The advantages and necessity of RDO over the conventional equi-strength design are addressed. Examples show that the proposed method has good stability and is efficient in dealing with the probabilistic optimal design of composite structures. It may serve as an effective tool to optimize other complicated structures with uncertainties.     

Orbit determination for the GOCE satellite

Precise Orbit Determination (POD) for the Gravity field and steady-state Ocean Circulation Explorer (GOCE), the first core explorer mission by the European Space Agency (ESA), forms an integrated part of the so-called High-Level Processing Facility (HPF). Two POD chains have been set up referred to as quick-look Rapid and Precise Science Orbit determination or RSO and PSO, respectively. These chains make use of different software systems and have latencies of 1 day and 2 weeks, respectively, after tracking data availability. The RSO and PSO solutions have to meet a 3-dimensional (3D) position precision requirement of 50 cm and a few cm, respectively. The tracking data will be collected by the new Lagrange GPS receiver and the predicted characteristics of this receiver have been taken into account during the implementation phase of the two chains.     

无人飞行器在线航迹规划技术研究

在线航迹规划是针对不确定环境中的航迹规划问题,无人飞行器需要在参考飞行航线的约束下,根据局部地形、地貌、障碍、威胁等信息以及飞机本身机动能力的限制,实时地计算出飞行航迹,并跟随该航迹完成飞行任务。阐述了无人飞行器在线航迹规划的结构框架,分析了无人飞行器动力学约束及威胁场约束,并根据航迹几何建模方法及在线规划算法的国内外研究概况,着重探讨了在线规划算法如A＊算法、Dynapath算法及连续型粒子群优化算法。最后,阐述了无人飞行器在线航迹规划面临的关键问题及发展趋势。     

吸收变异的粒子群算法及应用

提出了一种改进的粒子群算法,称为ConvergencemutationPSO(CM_PSO)。该算法根据粒子群中各粒子的适应值动态地调整粒子数量,把部分适应度差的粒子适当汇聚到适应度好的区域,在不损失全局搜索能力的同时,增强局部搜索能力。在将该算法应用于雷达组网误差配准问题中,提出了一种基于CM_PSO的误差配准技术。仿真表明,该算法能有效配准,与对比方法相比较,具有更好的收敛精度和更快的进化速度。     

基于三次样条插值的时滞GDM(1,2)模型构建及应用

针对具有时滞特征的输入输出系统,建立了灰色时滞GDM(1,2)模型。并结合三次样条插值和粒子群优化算法,求解出模型的3个参数[a,b,τ]。相对于先利用灰关联分析确定出时滞参数τ再求解灰预测模型的方法,把时滞参数τ融入模型中求解,避免了参数求解过程中的误差传递;并且还消除了时滞参数τ必须为整数的限制,使模型更加贴合现实中滞后期数不一定为整数的实际情况。最后将模型应用于公路旅客周转量的预测问题,实例表明该模型在具有时滞特征的输入输出系统预测中具有较高的精度。     

一种高稳定度帆板驱动系统的摩擦参数辨识

为了减少帆板驱动机构（SADA）对太阳帆板的激励作用和降低摩擦的影响,以永磁同步电机作为驱动源,提出了一种基于LuGre模型的摩擦参数辨识和补偿方法.为了能够准确的进行摩擦参数的试验辨识,设计了SADA系统摩擦力矩测试平台两者相结合进行了试验辨识,并将试验辨识所得到的摩擦模型参数代入SADA驱动系统模型中.通过仿真和试验表明：试验辨识较准确,且摩擦补偿后的SADA系统消除了正弦跟踪中速度过零时的波形畸变现象,能够有效改善SADA系统的动态性能     

考虑柔性检修计划的圆钢热轧批量调度

针对考虑柔性检修计划的圆钢热轧批量调度问题,构建了以最小化最大完工时间、订单提前及拖期总时长为目标函数的整数规划模型,用以制定有效的机器检修与批量生产协作计划。结合模型特征,提出一种改进多目标粒子群算法(IMPSO)实现求解。算法采用基于混沌加权适应度计算的插入式方法生成初始粒子群体;根据问题约束特征,设计修复规则对群体进化过程中产生的不可行粒子进行修复;采用精英策略保留算法迭代过程中的优势个体,并根据精英集合为每个粒子选择更新所需的极值;针对问题变量的离散特征,引入基于遗传操作的粒子更新方式。实验结果表明,模型和算法是可行和有效的。      

Longitudinal parameter identification of a small unmanned aerial vehicle based on modified particle swarm optimization

This paper describes a longitudinal parameter identification procedure for a small unmanned aerial vehicle(UAV)through modified particle swam optimization(PSO).The procedure is demonstrated using a small UAV equipped with only an micro-electro-mechanical systems(MEMS)inertial measuring element and a global positioning system(GPS)receiver to provide test information.A small UAV longitudinal parameter mathematical model is derived and the modified method is proposed based on PSO with selective particle regeneration(SRPSO).Once modified PSO is applied to the mathematical model,the simulation results show that the mathematical model is correct,and aerodynamic parameters and coefficients of the propeller can be identified accurately.Results are compared with those of PSO and SRPSO and the comparison shows that the proposed method is more robust and faster than the other methods for the longitudinal parameter identification of the small UAV.Some parameter identification results are affected slightly by noise,but the identification results are very good overall.Eventually,experimental validation is employed to test the proposed method,which demonstrates the usefulness of this method.     

一种基于粒子群优化的卫星仅测频被动定轨新算法

提出一种新的卫星对卫星仅测频被动定轨算法,采用粒子群优化算法(PSO)解决多维全局优化问题。首先,建立了卫星对卫星仅测频被动定轨的数学模型;其次,基于粒子群优化算法提出了对目标卫星轨道根数的估计方法;再次,推导了参数估计误差的克拉-美劳(CRLB)下限。最后通过计算机仿真对算法的性能进行了验证。多次仿真结果表明:该算法的参数估计误差接近克拉-美劳下限,且算法的运算量明显优于网格搜索法。