高超声速流动计算中LU-SGS隐式算法的应用研究

在高超声速条件下,对原始LU-SGS格式及其改进方法的收敛速度做了深入地比较分析,目的是进一步更好地将LU-SGS算法用于工程上复杂外形的计算模拟当中。二维圆柱,三维钝锥及空天飞机算例的结果表明：(i)对于高超声速粘性流动的计算,粘性项应进行隐式处理;(ii)BLU-SGS方法给出的内迭代方式的收敛性优于DP-LUR方法所给出的内迭代方式;(iii)LU-SGS算法中雅克比系数矩阵的计算方式对计算量及收敛性影响较大,若采用精确的矩阵形式则在流动无分离情况下能取得快速收敛的效果,而在含有流动分离的情况因受稳定性的影响精确的矩阵形式的收敛表现不及对角近似形式。     

基于输入匹配的直接自适应广义预测控制

提出了一种基于输入匹配的直接自适应广义预测控制器;分析证明了确定性系统和随机系统广义预测自适应控制算法的全局稳定性和收敛性。唯一的关键假设是参数已知时广义预测控制导致闭环系统稳定,去掉了正实条件的限制。     

一种无约束最优化方法——恰当共轭方向法

本文提出了一种求解无约束最优化问题的方法,称之为“恰当共轭方向法”。与通常的共轭方向法(如共轭梯度法)相比,这种方法可以节省大量的一维搜索工作量,并且还能提高收敛速率。     

带扩张观测器的三维有限时间收敛导引律

为了保证视线角速率在弹目碰撞前收敛到零附近的较小邻域内,从而达到准平行接近的状态,基于自抗扰控制的不确定性估计补偿思想,应用反演控制方法设计了一种考虑导弹自动驾驶仪二阶动态特性和目标机动的三维有限时间收敛导引律。根据有限时间收敛控制理论,严格证明了系统的有限时间收敛特性;为抑制量测噪声,将传统跟踪微分器进行改进并应用于扩张状态观测器与反演控制的设计中。仿真结果表明,在自动驾驶仪响应延迟情况下,所设计的导引律能够导引导弹在有限时间内精确地拦截高速机动目标;改进的跟踪微分器精度高、响应快;基于改进跟踪微分器的扩张观测器估计效果理想。     

变论域模糊自适应滑模有限时间收敛制导律

针对拦截高速机动目标的需求,研究了一种变论域模糊自适应滑模有限时间收敛制导律。推导了导弹-目标空间拦截模型,设计了三维滑模制导律;根据有限时间收敛制导律专家的经验,采用模糊自适应控制方法对滑模制导律的非切换项进行逼近,并设计了有限时间收敛模糊控制规则;提出了一种新型变论域伸缩因子,设计了基于新型伸缩因子的变论域模糊自适应滑模有限时间收敛制导律。仿真结果表明,所设计的制导律能够使导弹准确命中目标,并能够达到视线角速率有限时间收敛,且与比例制导律相比,具有更高的制导精度和更短的飞行时间。     

BP神经网络的收敛问题

研究了BP神经网络的收敛问题。基于随机理论 ,提出了解决网络收敛性问题的随机优选法。该方法不仅在任何条件下都能得到问题的具有一定精度的解答 ,而且收敛速度很快     

Adaptive random search for continuous simulation optimization

We present, analyze, and compare three random search methods for solving stochastic optimization problems with uncountable feasible regions. Our adaptive search with resampling (ASR) approach is a framework for designing provably convergent algorithms that are adaptive and may consequently involve local search. The deterministic and stochastic shrinking ball (DSB and SSB) approaches are also convergent, but they are based on pure random search with the only difference being the estimator of the optimal solution [the DSB method was originally proposed and analyzed by Baumert and Smith]. The three methods use different techniques to reduce the effects of noise in the estimated objective function values. Our ASR method achieves this goal through resampling of already sampled points, whereas the DSB and SSB approaches address it by averaging observations in balls that shrink with time. We present conditions under which the three methods are convergent, both in probability and almost surely, and provide a limited computational study aimed at comparing the methods. Although further investigation is needed, our numerical results suggest that the ASR approach is promising, especially for difficult problems where the probability of identifying good solutions using pure random search is small. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

光电载荷安装误差标校的飞行航路

无人机搭载光电载荷进行安装误差标校时,飞行航路的选择与规划在很大程度上影响着标校算法的收敛性与误差识别结果。为增强标校飞行架次的有效性,提高安装误差可被识别的程度,改善标校算法的收敛性及标校精度,对标校的原理和标校算法进行了理论分析,并利用考察差异函数偏微分的方法找到了标校航路和安装误差标校之间的相互联系,然后结合标校实施过程中所遇到的实际问题提出标校航路规划应遵循的原则,给出了标校航路规划的参考范例。     

基于组合牛顿迭代法的改进IEKF及其在UNGM中的应用

从高斯-牛顿迭代的角度对迭代扩展卡尔曼滤波(IEKF)进行分析,提出了一种基于组合牛顿迭代法的改进IEKF算法。该算法通过实时判断每次迭代对状态的逼近程度,采用加权平均的方法确定新的迭代值,继而采用卡尔曼滤波框架对状态进行量测更新。新算法较传统的IEKF具有精度高以及对初值不敏感的优点。实例仿真验证了该算法的有效性。     

Averaging frameworks for simulation optimization with applications to simulated annealing

We present two frameworks for designing random search methods for discrete simulation optimization. One of our frameworks is very broad (in that it includes many random search methods), whereas the other one considers a special class of random search methods called point‐based methods, that move iteratively between points within the feasible region. Our frameworks involve averaging, in that all decisions that require estimates of the objective function values at various feasible solutions are based on the averages of all observations collected at these solutions so far. Also, the methods are adaptive in that they can use information gathered in previous iterations to decide how simulation effort is expended in the current iteration. We show that the methods within our frameworks are almost surely globally convergent under mild conditions. Thus, the generality of our frameworks and associated convergence guarantees makes the frameworks useful to algorithm developers wishing to design efficient and rigorous procedures for simulation optimization. We also present two variants of the simulated annealing (SA) algorithm and provide their convergence analysis as example application of our point‐based framework. Finally, we provide numerical results that demonstrate the empirical effectiveness of averaging and adaptivity in the context of SA. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012