基于粒子群优化算法的平流层飞艇总体设计

平流层飞艇总重最小化能一定程度上反映总费用最低的设计目标,满足有效载荷功率需求是飞艇总体设计的出发点和落脚点。本文在飞艇参数建模中引入了太阳电池曲面铺装模型以及昼夜能源闭环模型;以整艇重量最小为设计目标,以昼夜能源供需平衡、浮重平衡、推阻平衡三大平衡问题为约束条件,运用粒子群优化（PSO）算法对飞艇的外形尺寸参数进行优化设计;分析了有效载荷功率需求和功率密度对于飞艇总体设计结果的敏感性。分析结果表明：（1）飞艇总重随有效载荷功率线性增加;（2）飞艇总重随有效载荷功率密度增加而迅速减小,但变化率逐渐变小,飞艇总重趋于稳定。最后,能源系统仿真的结果表明了飞艇总体设计方法的有效性和设计结果的临界特性。     

液压驱动型四足机器人对角小跑步态本体水平位置控制方法

为了对对角小跑中四足机器人本体的水平位置进行控制,建立基于沿支撑线方向运动分解的机器人近似动力学模型,将本体和对角支撑腿简化为一个七连杆结构和一个线性倒立摆,并且基于线性倒立摆解析模型提出摆动腿落足点位置计算方法,进而实现对本体水平位置的控制。针对液压作动器伸缩速度受限的问题,利用单腿冗余关节将关节角速度优化问题转化为标准二次型规划问题,通过设计二次型规划问题解法,降低对摆动腿关节角速度的需求,并且避免了传统伪逆方法可能产生的腿部奇异位型。仿真和实验结果表明:该方法能够实现在关节角速度受限的情况下,有效跟踪本体水平位置的期望轨迹。     

空间应急飞行器姿态快速机动控制研究

以空间应急飞行器姿态机动快速控制过程为研究对象,对姿态快速解耦控制问题进行研究.采用决策树理论对时间最优控制和倾斜开关曲线控制这两种控制策略进行综合,以控制时间作为代价函数,获得三通道时间最优控制策略.与其他方法相比,本控制策略控制时间大幅缩短,控制精度满足设计需求.另外对控制器不同控制周期下的控制效果进行仿真比较,对控制器控制周期选择具有指导意义.     

基于速度可控粒子群算法的航天测控系统任务可靠性分配

航天测控系统是一个典型的多阶段任务系统,讨论了可靠性分配时的约束条件和分配目标,构建了其任务可靠性分配模型,针对任务可靠性分配这类复杂的约束组合优化问题,提出了一种速度可控的粒子群优化算法.为克服粒子群算法的早熟问题,该算法引入了速度更新的方向控制规则和尺度控制规则用于增加群体的多样性,并根据两种控制规则,提出了种群粒子的速度更新策略.通过算例仿真,表明算法在用于航天测控系统任务可靠性分配问题时具有分配结果优、收敛速度快等优点.     

干扰及机动条件下的比例导引智能调控策略

由于红外诱饵干扰样式复杂、目标机动形式多变导致传统比例导引律极易被干扰。为提高采用比例导引方法的导弹性能,提出一种利用径向基函数网络调控比例系数及导弹发射时机的智能导引律。以飞行时间及脱靶量为参考,通过构建加权型指标函数将求解最优比例系数及发射时机问题转化为单目标优化问题;引入量子粒子群算法求解最优决策参量,并以其作为网络输出,干扰样式作为网络输入,离线训练径向基函数网络;为提高训练效率,结合K-means及K最近邻算法初始化径向基函数网络。仿真结果表明,当存在红外诱饵干扰时,智能导引律性能优于扩展比例导引律及自适应滑模导引律。     

基于不确定性的末制导初始参数优化设计方法

为量化无动力滑翔飞行器末制导初始参数不确定性的综合影响,提升飞行器落点精度,提出基于不确定性的末制导初始参数优化设计方法。面向飞行器末端高动态打击需求,采用落角约束下的滑模变结构导引律进行实时弹道成型,进而考虑末制导初始参数的不确定性。以落点有效毁伤半径概率和落点圆概率偏差为多优化目标,建立基于不确定性的末制导初始参数及制导律参数优化模型。针对这一不确定性优化模型,研究利用高效全局优化和蒙特卡洛方法,给出末制导初始参数及制导律参数的最优设计方案。仿真结果表明:该方法能显著提升落点精度,为方案设计阶段飞行器末制导交接点的选取提供决策支持。     

基于时间序无圈有向图的多准则优化成像调度

合理有效地利用遥感卫星资源获取更多高质量影像数据是卫星成像调度的重要工作。提出了一种新的成像调度解决方案。应用图论相关理论,建立卫星成像时间序无圈有向图模型,利用多项准则作为衡量标准对不同成像路径进行评价,提出时间序多准则最短路径算法求取优化成像路径。理论分析和实验表明,该解决方案可以在较短时间内获得多条pareto优化成像路径,具有良好的调度性能。     

Balancing a one‐way corridor capacity and safety‐oriented reliability: A stochastic optimization approach for metro train timetabling

In urban rail transit systems of large cities, the headway and following distance of successive trains have been compressed as much as possible to enhance the corridor capacity to satisfy extremely high passenger demand during peak hours. To prevent train collisions and ensure the safety of trains, a safe following distance of trains must be maintained. However, this requirement is subject to a series of complex factors, such as the uncertain train braking performance, train communication delay, and driver reaction time. In this paper, we propose a unified mathematical framework to analyze the safety‐oriented reliability of metro train timetables with different corridor capacities, that is, the train traffic density, and determine the most reliable train timetable for metro lines in an uncertain environment. By employing a space‐time network representation in the formulations, the reliability‐based train timetabling problem is formulated as a nonlinear stochastic programming model, in which we use 0‐1 variables to denote the time‐dependent velocity and position of all involved trains. Several reformulation techniques are developed to obtain an equivalent mixed integer programming model with quadratic constraints (MIQCP) that can be solved to optimality by some commercial solvers. To improve the computational efficiency of the MIQCP model, we develop a dual decomposition solution framework that decomposes the primal problem into several sets of subproblems by dualizing the coupling constraints across different samples. An exact dynamic programming combined with search space reduction strategies is also developed to solve the exact optimal solutions of these subproblems. Two sets of numerical experiments, which involve a relatively small‐scale case and a real‐world instance based on the operation data of the Beijing subway Changping Line are implemented to verify the effectiveness of the proposed approaches.     

A stochastic gradual cover location problem

Covering models assume that a point is covered if it is within a certain distance from a facility and not covered beyond that distance. In gradual cover models it is assumed that a point is fully covered within a given distance from a facility, then cover gradually declines, and the point is not covered beyond a larger distance. Gradual cover models address the discontinuity in cover which may not be the correct approach in many situations. In the stochastic gradual cover model presented in this article it is assumed that the short and long distances employed in gradual cover models are random variables. This refinement of gradual cover models provides yet a more realistic depiction of actual behavior in many situations. The maximal cover model based on the new concept is analyzed and the single facility location cover problem in the plane is solved. Computational results illustrating the effectiveness of the solution procedures are presented. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

串联系统预防维修周期优化策略研究(英文)

针对串联系统建立了基于可靠性约束的预防性维修优化模型,该模型充分考虑到预防维修可以提高系统可靠性的同时,其故障率会随着维修次数的增加而上升,引入役龄回退因子对预防维修活动前后系统性能的动态变化进行了描述.通过实例验证了该优化模型的正确性与可用性,对模型作进一步的扩展,还可用于更一般复杂的串并联系统.