A note on the value function for constrained control problems

We consider the Mayer optimal control problem with dynamics given by a nonconvex differential inclusion, whose trajectories are constrained to a given set and we obtain a relation between the costate function that appears in the maximum principle and the value function. This relation extends the known conditions existing in the literature for unconstrained problems to those for problems under state constraints.     

Night and Day: Are Siblings as Different in Temperament as Parents Say They Are?

Twin studies suggest that parent ratings of temperament exaggerate differences between twins. The present study examined whether such contrast effects also operate for nontwin siblings. The activity level (AL) and shyness of 95 nontwin sibling pairs (ages 3 to 8 years) were assessed via parent ratings and objective measures (actigraph and observer ratings). Siblings showed no resemblance in either parent-rated AL or shyness; however, sibling resemblance for actigraph AL and observer-rated shyness was substantial. Thus, parents do contrast their nontwin siblings when rating these 2 temperament dimensions. Moreover, the importance of sibling differences in temperament to the sibling relationship and differential maternal treatment varied across the different measures of AL and shyness, suggesting that parent perceptions may play a role in these associations. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

具有完整性的状态反馈控制

本文基于矩阵方程解，提出了一种开环稳定系统反馈的设计方法，所设计的系统对任意传感器失效具有完整性。     

关于可分2－D系统的几个注记

可分２－Ｄ系统是一类具有良好特性的重要的特殊２－Ｄ系统，它在多方面均有重要的应用．本文在已有结果的基础上对具有可分性的２－Ｄ多输入多输出系统在再实现问题、渐近稳定性代数判据、状态观测器设计等方面进行了较为广泛的研究，得到了许多较好的结果．     

用于机器人轨迹定位纠正系统的回声状态神经网络研究

循环神经网络(RecurrentNeuralNetworks)是人工神经网络(ArtificialNeuralNetworks)中重要的分支,与前馈神经网络(ForwardNeuralNetworks)相比具有更好的时间序列学习能力。但长期以来其学习法一直不能脱离前馈神经网络而自成一体,回声状态神经网络(EchoStateNetworks(ESN))是打破这一局面的全新学习方法。其独特的结构,良好的短期记忆能力,方便的学习方法,不俗的非线性特性是以前循环神经网络所不可比的。本文在介绍了回声状态神经网络之后将其用于四轮机器人的位置测量系统中,有良好的表现。     

化工软测量技术研究进展

详细说明了软测量技术的含义以及软测量系统的组成。在此基础上，介绍了软测量技术中基于机理分析建模、应用状态观测器建模、应用统计分析建模、神经元网络、模糊技术、以及支持向量机的智能建模等软测量建模理论方法和最新研究成果，之后介绍了小波分析、推断控制算法在软测量数据处理中的应用，最后介绍了基于虚拟仪器开发平台的软测量系统实现技术。     

Observer design for autonomous discrete-time nonlinear systems

We obtain a necessary and sufficient condition for a discrete-time nonlinear system to be locally state equivalent to the nonlinear observer form. The result looks similar to the continuous counterpart except for the fact that Ad-operation is utilized instead of ad-operation.     

Florida DOT Project-Level Bridge Management Models

The Florida Department of Transportation (FDOT) is in the process of implementing Pontis, a Bridge Management System, to provide decision support to engineers in the headquarters and district offices as they make routine policy, programming, and budgeting decisions regarding the preservation and improvement of the state’s bridges. As part of this effort, an ongoing research program is underway to adapt the system to FDOT needs as well as to advance the state of the art in several areas important to the Department. Most of the research results are organized around a new project-level decision support framework that complements and builds on Pontis’ existing network-level analysis. Specific new models include accident risk and user cost due to roadway width and alignment deficiencies; user cost of load capacity and vertical clearance restrictions, and moveable bridge openings; project-level prediction models for bridge element condition and costs; and prediction of economics of scale and scoping possibilities. The new models are built into a highly graphical spreadsheet model for decision support use.     

DESIGN AND IMPLEMENTATION OF STATE OBSERVERS IN REACTIVE DISTILLATION

This study demonstrates that state observers can be developed and applied to infer the composition profiles of reactive distillation columns from noise-contaminated temperature measurements. The design and implementation of a Kalman filter (KF) and a Luenberger observer (LO) are carried out, and their performances are quantitatively assessed. The reliability, accuracy, and robustness of the two designs method are examined and compared quantitatively. The design and implementation of a Luenberger observer are simpler and easier to carry out than those of a Kalman filter. On the other hand, a Kalman filter is found to be more robust to a noisy measurements, erroneous initial estimates, and model uncertainties. A Luenberger observer could be used for composition estimation of reactive distillation when an ideal model of the system can reasonably approximate the real system; otherwise, a Kalman filter is recommended to be applied in more practical situations.     

Analysis of the Datagram Congestion Control Protocol’s connection management procedures using the sweep-line method

State space explosion is a key problem in the analysis of finite state systems. The sweep-line method is a state exploration method which uses a notion of progress to allow states to be deleted from memory when they are no longer required. This reduces the peak number of states that need to be stored, while still exploring the full state space. The technique shows promise but has never achieved reductions greater than about a factor of 10 in the number of states stored in memory for industrially relevant examples. This paper discusses sweep-line analysis of the connection management procedures of a new Internet standard, the Datagram Congestion Control Protocol (DCCP). As the intuitive approaches to sweep-line analysis are not effective, we introduce new variables to track progress. This creates further state explosion. However, when used with the sweep-line, the peak number of states is reduced by over two orders of magnitude compared with the original. Importantly, this allows DCCP to be analysed for larger parameter values. Somsak Vanit-Anunchai was partially supported by an Australian Research Council Discovery Grant (DP0559927) and Suranaree University of Technology. Guy Edward Gallasch was supported by an Australian Research Council Discovery Grant (DP0559927).