类蛛网状盘式谐振微陀螺的抗冲击性能与结构误差仿真分析

具有导航级潜能的盘式谐振微陀螺是国际研究最热门的一类微机械陀螺,在此基础上设计了一款频率裂解小的类蛛网状盘式谐振微陀螺。通过有限元仿真软件进行了模态分析和冲击分析,仿真结果表明类蛛网状盘式谐振微陀螺工作模态与寄生模态最小频差为3.9kHz,可承受的冲击载荷加速度高达25000g。此外,开展了晶向误差和工艺误差等结构误差对频率裂解影响的仿真研究,结果表明,其比圆环状盘式谐振陀螺在频率裂解上具有更低的结构误差敏感度。综上说明该结构能有效抑制寄生模态干扰、抗冲击性能强且结构误差鲁棒性好,具有较好的发展前景。     

考虑微加工误差的微机械陀螺的健壮性优化

 为减小微加工误差对音叉振动式微机械陀螺设计性能的影响、提高其批量性能稳定性,以对其加工后性能变异影响最大的若干关键参数为设计变量,在分析了几何约束条件、模态性能约束条件、基于模态的加工灵敏度约束条件的基础上建立了微机械陀螺健壮性优化的数学模型。在无需事先知道微机械陀螺尺寸／性能的详尽统计信息的条件下,采用一阶摄动技术和遗传算法实现了微机械陀螺的健壮性优化设计。     

Periodic Error Compensation for Quartz MEMS Gyroscope Drift of INS

In order to improve the navigation accuracy of an inertial navigation system （INS）, composed of quartz gyroscopes, the existing real-time compensation methods for periodic errors in quartz gyroscope drift and the periodic error term relationship between sampled original data and smoothed data are reviewed. On the base of the results, a new compensation method called using former period characteristics to compensate latter smoothness data （UFCL for short） method is proposed considering the INS working characteristics. This new method uses the original data without smoothing to work out an error conversion formula at the INS initial alignment time and then compensate the smoothed data errors by way of the formula at the navigation time. Both theoretical analysis and experimental results demonstrate that this method is able to cut down on computational time and raise the accuracy which makes it a better real-time compensation approach for periodic error terms of quartz micro electronic mechanical system （MEMS） gyroscope＇s zero drift.     

一种基于余数周期的 PRI精确估计算法

针对传统脉冲重复间隔（PRI）分选算法在估计PRI方面存在的不足,提出了一种对PRI周期信号的周期进行精确估计的算法。该算法首先从待分选脉冲序列中提取出属于一部雷达的脉冲样本,然后利用同余方程的余数周期性质对该雷达脉冲序列的PRI进行精确的估计。相对于传统的PRI估计算法,该算法有效地消除了TOA量化误差对PRI估计造成的影响,可以精确地估计出雷达脉冲序列的准确 PRI数值,从而能够更好地满足信号分选算法的处理需求。理论推导及仿真实验均表明了该算法的有效性。     

分析插值误差和斜率的轨迹优化网格细化方法

提出一种基于插值误差和斜率分析的轨迹优化自适应网格细化方法,包括节点插入算法和节点删除算法。节点插入算法分析各个离散节点的控制变量的插值误差。若插值误差较大,则在该节点周围增加节点细化网格;否则,不进行细化。节点删除算法分析各个离散节点处的控制变量斜率。若某个节点的左斜率和右斜率都为零,那么删除该节点;否则,保留该节点。采用三个典型的轨迹优化算例验证了所提出的方法的有效性和特色,并且与其它几种网格细化方法进行了对比。仿真结果表明,本文方法生成的网格规模较小,需要的网格迭代次数较少,能够快速、高精度求解非光滑轨迹优化问题。     

基于IPSO-Elman神经网络的飞机客舱能耗预测

为了提高飞机客舱使用地面空调制冷时客舱能耗的预测精度,提出了一种改进的粒子群优化（IPSO）Elman神经网络的飞机客舱能耗预测模型。依据对算法中惯性权重与学习因子的收敛域分析,得出了二者合理的取值范围,将粒子到全局最优位置间距离与参数的取值范围相结合,构造了惯性权重与学习因子的动态调节函数,对其进行非线性的动态调节,并引入了变异因子,提出了一种跳出局部最优的策略,防止粒子群优化（PSO）陷入局部最优。将IPSO-Elman应用于Boeing738飞机客舱能耗预测中,与PSO-Elman、Elman算法进行性能比较,仿真结果表明基于IPSO-Elman的客舱能耗预测模型在预测精度和收敛速度方面均有一定的提升。该研究结果为飞机客舱能耗预测模型的建立提供了理论依据,对飞机地面空调的节能与机场电能合理调配提供了支持。     

多重应力下滚动轴承剩余寿命预测

为了实现多重应力下滚动轴承的剩余寿命预测,有效利用不用应力下的退化数据,提出了一种基于加速模型和贝叶斯（Bayesian）理论的滚动轴承剩余寿命预测方法。通过拟合优度检验和威布尔（Weibull）概率图检验法对滚动轴承试验中的数据进行有效性分析。利用switching Kalman filters(SKF)判断滚动轴承各时刻的退化状态。当滚动轴承进入加速退化时,用指数模型拟合轴承退化过程,利用广义线性对数模型表示退化模型参数与应力的关系,根据修正后的轴承实时退化数据利用贝叶斯算法更新模型参数,得到滚动轴承剩余寿命的概率密度函数,从而实现滚动轴承剩余寿命预测。采用XJTU-SY轴承数据集进行验证,预测结果的均方根误差在20 min以内,证明该方法能够有效预测滚动轴承的剩余寿命。     

Comparison between observed ionospheric foF2 and IRI-2001 predictions over periods of severe geomagnetic activities at Grahamstown,South Africa

The observed ionospheric F2 critical frequency (foF2) values over a South Africa mid-latitude station, Grahamstown, (geographic coordinates: 33.3°S, 26.5°E), were analysed and compared with International Reference Ionosphere (IRI) model, using the CCIR (Comite´ Consultatif International des Radio communications) and URSI (Union Radio-Scientifique Internationale) coefficients, during four geomagnetically disturbed days in the year 2000. These days are April 5, May 23, August 10 and September 15. The data were analysed for five days around the storm day. Comparisons between the IRI-2001 predicted foF2 values, using both CCIR and URSI coefficients and the observed values are shown with their root-mean-square error (RMSE) and the relative deviation module mean (rdmm) for the various storm periods. The CCIR option performed more accurately than the URSI option.     

Using the radial basis function neural network to predict ionospheric critical frequency of F2 layer over Wuhan

Neural networks (NNs) have been applied to ionospheric predictions recently. This paper uses radial basis function neural network (RBF-NN) to forecast hourly values of the ionospheric F₂ layer critical frequency(f_oF₂), over Wuhan (30.5N, 114.3E), China. The false nearest neighbor method is used to determine the embedding dimension, and the principal component analysis (PCA) is used to reduce noise and dimension. The whole study is based on a sample of about 26,000 observations of f_oF₂ with 1-h time resolution, derived during the period from January 1981 to December 1983. The performance of RBF-NN is estimated by calculating the normalized root-mean-squared (NRMSE) error, and its results show that short-term predictions of f_oF₂ are improved.     

考虑侧窗约束的模型预测静态规划末制导方法

针对红外导引头侧窗探测模式下,非对称视场约束造成末制导阶段目标易丢失的问题,提出一种考虑侧窗约束的模型预测静态规划末制导方法。首先,基于体视线坐标系建立三维相对运动模型,得到不依赖于“小攻角”假设的准确模型。在处理无过程约束问题的模型预测静态规划方法基础上,引入松弛变量与虚拟控制量,设计出考虑侧窗视场约束的末制导算法。为了进一步降低末制导算法对初始猜测轨迹的依赖性,提高适应性与计算效率,提出逐步增加约束条件的计算策略。仿真结果表明,该方法在末制导过程中满足侧窗约束,相比于凸优化方法,优化变量减少,计算速度更快;相比于基于障碍李雅普诺夫函数的末制导律,能够满足侧窗约束,同时能适应不同的初始条件。