突发GMSK混和信号混合位置估计迭代算法

针对GMSK混合信号的混合位置估计问题,提出了一种基于参数估计与混合位置估计的迭代算法。混合信号混合部位和未混合部位在幅度和频率上存在明显差异,通过混合部位信号过零点和极值点得到混合的信号和幅度估计后,可以对采样点是否混合进行判断,同时混合起点对应着相对时延,当混合信号的相对时延较大时,利用未混合部位信号来进行迭代时,其估计精度更高。对混合起点估计的克拉美罗界进行了推导,仿真表明,本文算法估计精度高,所需的数据量小,适用于突发通信,且对参数变化不敏感,具有较强的鲁棒性,在高信噪比下混合位置估计精度接近克拉美罗界。     

神经网络在聚合过程中的应用

介绍了神经网络在聚合反应过程中的建模，预测、优化，控制和故障诊断及模式识别方面的应用，讨论了其优点和局限性，并分析今后神经网络的发展趋势。     

Tensile properties of long aligned double-walled carbon nanotube strands

The mechanical properties of well-aligned double-walled carbon nanotube (DWNT) strands with diameters of 3-20 μm and lengths of ∼10 mm were measured using a stress-strain puller. The average tensile strength and Young’s modulus of the tested strands are 1.2 GPa and 16 GPa, respectively. Deformation and fracture processes of these samples are discussed. The tensile strength and Young’s modulus of an individual DWNT bundle were estimated, with values comparable to those of SWNT bundles. The superior mechanical strengths of our as-prepared DWNT strands are expected to give them potential as a high-strength material and a reinforcement in composites.     

NURBS曲线C~1连续参数优化算法

曲线的参数特性直接决定基于自由曲线的路径规划、运动控制等算法的质量.为了生成满足C1连续的近似弧长参数化,提出一种基于分段三次重新参数化的参数优化算法.首先利用Simpson方法离散积分能量,然后使用极值求解法求得初始解,最后通过LM(Levenberg-Marquardt)优化算法计算出曲线的最优参数表示.与C1连续的分段有理重新参数化方法相比,该算法能够在分段数量很少的情况下达到局部最优.最后通过实例说明了文中算法的有效性.     

Cooperative Multi-Agent Reinforcement Learning with Constraint-Reduced DCOP

Cooperative multi-agent reinforcement learning (MARL) is an important topic in the field of artificial intelligence,in which distributed constraint optimization (DCOP) algorithms have been widely used to coordinate the actions of multiple agents.However,dense communication among agents affects the practicability of DCOP algorithms.In this paper,we propose a novel DCOP algorithm dealing with the previous DCOP algorithms' communication problem by reducing constraints.The contributions of this paper are primarily threefold:① It is proved that removing constraints can effectively reduce the communication burden of DCOP algorithms.② An criterion is provided to identify insignificant constraints whose elimination doesn't have a great impact on the performance of the whole system.③ A constraint-reduced DCOP algorithm is proposed by adopting a variant of spectral clustering algorithm to detect and eliminate the insignificant constraints.Our algorithm reduces the communication burdern of the benchmark DCOP algorithm while keeping its overall performance unaffected.The performance of constraint-reduced DCOP algorithm is evaluated on four configurations of cooperative sensor networks.The effectiveness of communication reduction is also verified by comparisons between the constraint-reduced DCOP and the benchmark DCOP.     

Research on Variable Structure and Adaptive Control for Strap-on Rocket

固体捆绑运载火箭变结构自适应控制

陈曦,刘玉玺,汪轶俊

（上海宇航系统工程研究所）

创新点说明：

对于新型固体捆绑火箭而言,由于箭体安装固体助推器,存在推力偏差较大、推力同步性较大的问题,导致火箭的结构干扰增大。安装尾翼外干扰增大,如经过大风区时,风干扰对箭体产生较大的影响。同时考虑建立动力学模型的过程中存在的参数不确定性、参数摄动、三通道间耦合的问题,传统的控制方法难以满足新型固体捆绑火箭的要求,因此本文率先将基于模型参考的变结构自适应控制方法应用于固体捆绑运载火箭上。

研究目的：

本文针对固体捆绑运载火箭控制系统的特点,应用变结构自适应控制理论,设计适用于固体捆绑运载火箭的控制系统,保障固体捆绑运载火箭的正常飞行。

研究方法：

（1）建立固体捆绑运载火箭动力学模型,考虑模型中参数不确定性、未建模动态及通道间耦合;

（2）分析固体捆绑运载火箭存在的干扰,例如：四个固体助推带来的推力偏差、推力不同步性、推力线偏斜,质心横移、芯级、发动机及尾翼安装误差、芯级发动机液体燃料晃动、箭体弹性振动等各类结构误差及风干扰等外干扰;

（3）针对固体捆绑运载火箭设计变结构自适应控制律的设计并证明其稳定性;

（4）Simulink仿真分析验证。

结果：

本文使用变结构自适应控制理论设计的控制系统可全局渐进稳定,三通道姿态角跟踪误差较小,姿态角收敛速度较快,系统性能良好,可有效处理各类结构干扰、外干扰、气动参数不确定性及未建模动态等问题,该控制系统的动态特性及控制品质良好,具有工程应用的前景。

结论：

本文将基于模型参考的变结构自适应控制方法应用于新型固体捆绑运载火箭的控制系统的设计中。给出了仅有俯仰方向有大姿态变动的条件下的火箭动力学偏量模型,并在此基础上设计变结构自适应控制律,同时证明了该控制系统的稳定性。本文通过理论及仿真分析,证明了变结构自适应控制理论在新型固体捆绑运载火箭上的应用可行性,具有一定的工程应用前景。

关键词：运载火箭;模型参考;变结构控制;自适应控制

     

基于Zadoff-Chu序列的移动数字电视接收系统频偏估计算法

针对移动数字电视接收系统频偏估计算法存在的频偏估计均方差和误码率高等问题,提出了一种基于Zadoff-Chu （ZC）序列频偏估计算法。首先通过建立信号模型,利用仿真分析了载波频率偏差对接收系统影响;其次,信标结构中的两个同步信号以半周期形式选择ZC序列,1个导频符号序列由2个ZC序列构成。因此,接收端接收的2个符号是完全相同的ZC序列,通过对1个ZC序列半周期进行自相关运算并计算频偏值,即可以完成对整个周期符号的频偏估计。仿真结果表明,相比于其它算法,频偏估计均方差可降低20~30%,增益提升约2dB。     

Soft sensor modeling method based on hybrid model of nearest neighbor and neural network

Soft-sensing modeling can effectively solve the problems of large measurement lag, high price, and complex maintenance of online analytical instruments in the production process. At present, neural network based on data-driven is one of the main tools of soft sensor. In the process of modeling data collection, the collection of dominant variables is much more difficult than that of auxiliary variables, resulting in a large amount of unlabeled data. However, traditional soft sensor modeling methods ignore these unlabeled data and only use a small amount of labeled data for modeling, which has negative effect on the prediction accuracy of the model. To solve the problem of label missing, the nearest neighbor algorithm is used to pseudo label the unlabeled data. At the same time, a network structure is designed by combining convolution operation and gated recurrent unit neural network (GRU) to further utilize the unlabeled data, extract the dynamic feature from data at different time, and improve the prediction accuracy of the neural network. Finally, the method is applied to the prediction of propane concentration on the top of propylene distillation column. The results show that the model can solve the problem of label missing in the nonlinear dynamic system and has higher prediction accuracy.     

基于改进UKF的滚动时域估计方法及其在PVC聚合过程中的应用

聚合过程具有高度非线性和时变性等特点,参数在线估计有助于聚合过程控制性能和优化效果的改善。滚动时域估计(MHE)方法是一种用于聚合过程参数和状态估计的有效方法。本文提出了一种基于改进无迹卡尔曼滤波(UKF)的滚动时域估计方法,用于氯乙烯聚合过程机理模型时变参数的估计。滚动时域估计方法的关键问题之一是抵达成本(Arroval Cost)的近似估算,文中采用2种采样策略来实现抵达成本的自适应计算和更新。将提出的方法应用于氯乙烯聚合过程传热系数的在线估计,并与传统的滚动时域估计方法相比较,体现了该方法的有效性。     

低复杂度递推信道估计联合均衡算法

精确的信道状态信息对单载波频域均衡(Sin gle carrier frequency domain equalization, SC FDE)非常重要,本文基于高斯消息传 递 (Gaussian message passing,GMP)提出了一种递推最小二乘(Least squares, LS)信道估计 算法。借助于因子图,根据广义分配率思想,将估计函数分为多个局部函数,每个局部函数 做多利处理,然后通过定义辅助变量使其成递推关系。根据turbo原理,迭代交换软信息, 使得估计、均衡及译码联合进行。在此基础上 ,分析并推导出无偏简化方法,然后借助于快速傅里叶变换,使得算法复杂度随观察向量长 度的增加呈对数上升。仿真表明该简化算法具有较好的信道估计性能和误码率特性的同时, 显著降低了计算量。