基于连续反演算法的时滞补偿控制综述

在实际系统的工作过程中,时滞现象普遍存在,如控制信号的采集与传输、控制器的构建与实施、事件的决策与处理等。考虑并有效处理时滞特性的影响有助于提升系统的性能。基于连续反演算法的时滞补偿控制策略是一种有效的控制方法且取得很多研究成果。该时滞补偿控制的主要思路是将具有时滞特性的常微分方程或偏微分方程变换为不具有时滞特性的常微分方程?偏微分方程/常微分方程?偏微分方程(ODE?PDE/PDE?PDE)级联系统。进一步地,基于变换的级联系统,结合连续反演算法提出相应的控制策略。该方法具有系统的稳定性证明简单,鲁棒性强,易于求取闭环系统精确解等优点。详细论述了连续反演算法的基本原理,并针对基于连续反演算法的时滞补偿控制算法在处理输入、输出、状态等类型时滞特性的最新研究进展做简单的阐述和总结。最后,开放式地论述了时滞系统的未来研究方向。      

Effects of can parameters on canned—forging process of TiAl base alloy（Ⅱ）——Mechanical behavior

1　ＩＮＴＲＯＤＵＣＴＩＯＮＴｉＡｌａｌｌｏｙｈａｓｌｏｎｇｂｅｅｎｃｏｎｓｉｄｅｒｅｄａｓａｐｒｏｍｉｓ ｉｎｇｍａｔｅｒｉａｌｆｏｒｈｉｇｈｔｅｍｐｅｒａｔｕｒｅａｐｐｌｉｃａｔｉｏｎ ,ｄｕｅｔｏｉｔｓｅｘｃｅｌｌｅｎｔｈｉｇｈｔｅｍｐｅｒａｔｕｒｅ ｐ     

基于PCA和径向基函数神经网络的滚动轴承故障模式的识别

根据径向基函数神经网络的优点，在对滚动轴承振动信号故障特征分析的基础上，提出一种应用主矢量分析(PCA)将高维相关变量转化为低维独立变量，利用其特征值建立径向基函数神经网络的方法，并用该网络对滚动轴承的故障模式进行了识别。理论和试验证明了该方法的有效性，且具有较高的识别精度。     

输油管道失效导致的环境污染

石油是地球赋予人类的宝贵能源财富,管道输送是石油的主要输送方式。输油管道的失效不论破裂、穿透和小孔穿透,都将引起介质的泄漏。各种石油制品的挥发、不完全燃烧物飘落等引起一系列石油污染问题。这些石油类物质进入土壤环境后,会发生许多物理、化学和生化作用,造成土壤水环境系统的严重污染。本文根据引起管道失效的多种因素,制订了相应的防治措施,将事故隐患消灭在萌芽状态,避免事故的发生,以防患于未然。     

固相萃取净化-气相色谱-质谱法测定染料中7种指示性多氯联苯

本文建立了以固相萃取(SPE)前处理净化、结合气相色谱-质谱(GC-MS)测定染料中7种指示性多氯联苯的分析方法。该法将染料样品经溶剂萃取后,通过固相萃取(SPE)净化、浓缩,气相色谱-质谱采用选择离子监测模式(SIM)测定,以保留时间和特征离子定性,外标法定量。     

Robust adaptive control of a thruster assisted position mooring system

In this paper, robust adaptive control is developed for a thruster assisted position mooring system in the transverse direction. To provide an accurate and concise representation for the dynamic behavior of the mooring system, the flexible mooring lines are modeled as a distributed parameter system of partial differential equations (PDEs). The proposed control is applied at the top boundary of the mooring lines for station keeping via Lyapunov’s direct method. Adaptive control is designed to handle the system parametric uncertainties. With the proposed robust adaptive control, uniform boundedness of the system under the ocean current disturbance is achieved. The proposed control is implementable with actual instrumentations since all the signals in the control can be measured by sensors or calculated by using a backward difference algorithm. The effectiveness of the proposed control is verified by numerical simulations.     

八羰基二钴的合成

本文考察了反应起始原料、催化剂及其加入量、溶剂和压力对八羰基二钴合成的影响，得出了最佳的合成条件．     

Human tracking system based on adaptive multi-feature mean-shift for robot under the double-layer locating mechanism

Human tracking has been a challenging task for robot in the past decades. In this paper, to realize the human following in a cluttered environment, a human tracking system based on adaptive multi-feature mean-shift (AMF-MS) under the double-layer locating mechanism (DLLM) is proposed to solve the problem of distinguishing target, occlusion, and quick turning. The DLLM, considering the course location processing and fine location processing, is designed to estimate the person’s position using the fusion of heterogeneous data. As an ID tag attached on target can be detected by RF antennas, the course locating method can track the target easily and quickly. The Bayes rule is introduced to calculate the probability where the tag exists due to the instability of RF signals. In the fine locating step, the AMF-MS is proposed because it can reduce computational load and represent target by multi-feature histogram function. Meanwhile, we combine extended Kalman filter and AMF-MS to overcome MS’s inability of occlusion. To control the robot following the target person precisely, an intelligent gear shift strategy based on fuzzy control is implemented by analyzing the robot structure. Experiments demonstrate that the proposed approach is robust to handle complex tracking conditions, and show the system has an optimum performance.     

生物发酵过程中SSVIP-NNI软测量方法

生物发酵过程中关键生化变量难以直接检测,提出了一种利用基于敏感性与特异性的变量投影重要性(SSVIP)方法优化神经网络逆系统(NNI)的软测量方法。根据逆系统理论建立软测量模型,采用VIP变量优选方法,对软测量模型中的辅助变量进行优化。为了进一步提高VIP方法优选变量的性能,利用模型敏感性与特异性的概念,重新定义了VIP筛选阈值,确定最优的发酵过程软测量模型的变量。构造神经网络近似最优逆系统软测量模型,实现对发酵过程中关键过程变量的估计。通过Pensim仿真平台进行实验研究,结果表明经过辅助变量优选的神经网络逆系统软测量模型具有更高的估计精度和泛化能力。     

基于CARS变量筛选的固态发酵pH值近红外检测

为了提高近红外光谱技术快速检测固态发酵过程中pH值的精度和稳定性,提出了采用竞争自适应重加权采样(competitive adaptive reweighted sampling,CARS)法筛选出与pH相关的波长变量建立PLS预测模型,对验证集样本进行预测的方法。并与2种常见的变量筛选法GA-PLS和蒙特卡罗无信息变量消除法(MC-UVE)相比较.实验结果表明:CARS方法能有效筛选有用波长26个变量建立PLS模型,其校正集交互验证均方根误差(RMSECV)以及交互验证相关系数(Rc)分别为0.0368和0.9950;验证集的预测均方根误差(RMSEP)以及预测相关系数(Rp)分别为0.0589和0.9895。