基于分布式粒子群算法的翼型优化设计

采用求解N-S方程作为优化算法中的CFD分析方法,基于标准粒子群优化算法(PSO),将其与遗传算法中的选择机制相结合,形成了一种改进的基于自然选择的粒子群算法(SELPSO),以提高算法的求解精度和改善算法的全局收敛性。为改善串行粒子群算法效率低,耗机时等缺点,文中将分布式计算引入到优化设计过程中,实现了基于分布式粒子群算法的翼型设计优化系统,设计实践表明,文中发展的优化算法对优化设计系统质量和效率都有着大幅度的提高,在工程中具有很好的实用价值。     

Model parameters estimation of aero-engine based on hybrid optimization algorithm

A hybrid optimization algorithm for the time-domain identification of multivariable,state space model for aero-engine was presented in this paper.The optimization procedure runs particle swarm optimization (PSO) and least squares optimization (LSO) "in series".PSO starts from an initial population and searches for the optimum solution by updating generations.However,it can sometimes run into a suboptimal solution.Then LSO can start from the suboptimal solution of PSO,and get an optimum solution by conjugate gradient algorithm.The algorithm is suitable for the high-order multivariable system which has many parameters to be estimated in wide ranges.Hybrid optimization algorithm is applied to estimate the parameters of a 4-input 4-output state variable model (SVM) for aero-engine.The simulation results demonstrate the effectiveness of the proposed algorithm.      

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。     

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。     

基于改进粒子群的绳驱连续型机械臂逆运动学分析

绳驱连续型机械臂具有良好的柔性和灵活性,能够在狭小的空间内运动,但相对于离散型机械臂,利用D-H参数构建运动学模型的方法不再适用,且连续型机械臂具有无穷自由度,逆运动学求解困难。针对一种两节的绳驱连续型机械臂的运动学问题,基于分段常曲率假设,建立了运动学模型,并分析了两节之间的运动学耦合关系。在此基础上,利用改进粒子群优化算法求解连续型机械臂逆运动学,通过仿真验证了该逆解算法的可行性和有效性,搭建了绳驱连续型机械臂的物理样机系统,并验证了基于分段常曲率假设的正运动学模型的正确性。     

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。