移动机器人在障碍物具有不确定性时的运动规划

本文提出了一种移动机器人在实时避碰中对移动障碍物的运动不 确定性的处理方法.该方法主要考虑了两个不确定性来源：移动障碍物运动速度上的不确定 性和运动方向上的不确定性.用概率统计的方法来为不确定性建模.并与一种基于相对速度的 在线避碰方法结合起来对移动障碍物避碰.通过这种方法可以使机器人对移动障碍物的避碰 更有效率.     

Concurrent treatment of parametric uncertainty and metamodeling uncertainty in robust design

Robust design is an effective approach to design under uncertainty. Many works exist on mitigating the influence of parametric uncertainty associated with design or noise variables. However, simulation models are often computationally expensive and need to be replaced by metamodels created using limited samples. This introduces the so-called metamodeling uncertainty. Previous metamodel-based robust designs often treat a metamodel as the real model and ignore the influence of metamodeling uncertainty. In this study, we introduce a new uncertainty quantification method to evaluate the compound effect of both parametric uncertainty and metamodeling uncertainty. Then the new uncertainty quantification method is used for robust design. Simplified expressions of the response mean and variance is derived for a Kriging metamodel. Furthermore, the concept of robust design is extended for metamodel-based robust design accounting for both sources of uncertainty. To validate the benefits of our method, two mathematical examples without constraints are first illustrated. Results show that a robust design solution can be misleading without considering the metamodeling uncertainty. The proposed uncertainty quantification method for robust design is shown to be effective in mitigating the effect of metamodeling uncertainty, and the obtained solution is found to be more “robust” compared to the conventional approach. An automotive crashworthiness example, a highly expensive and non-linear problem, is used to illustrate the benefits of considering both sources of uncertainty in robust design with constraints. Results indicate that the proposed method can reduce the risk of constraint violation due to metamodel uncertainty and results in a “safer” robust solution.     

足球机器人动态路径规划方法研究

针对机器人足球系统的高度实时性、不确定性,提出了一种基于统计预测的路径规划方法,该方法考虑到障碍物的速度大小和方向的不确定性,用数学统计的方法对障碍物的运动进行建模;机器人在运动过程中,根据得到的环境信息在机器视觉范围内建立预测窗口和避障窗口,在预测窗口内,机器人根据障碍物的信息建立障碍物的预测区域,在避障窗口内,机器人根据自身的位置与障碍物的预测区域,分别调用切线法或滚动窗口法进行路径规划;该方法属于局部路径规划方法,机器人在移动过程中需要不断更新环境信息来进行避障.     

基于蒙特卡罗法的平面度测量不确定度评定

形位误差的测量不确定度评定是目前测量领域研究的热点;但由于其测量的复杂性和测量结果评定的多样性,导致在实际测量结果中形位误差测量的不确定度评定成了难题;为此,根据形状误差评定准则,选取最小二乘法建立数学模型,确定形状误差数学模型中各参数值的传递系数和单点不确定度,并分析具体的测量方法和测量过程中的不确定度来源,根据传统的GUM法对其进行不确定度评定;然后采用蒙特卡罗伪随机数的方法来模拟实际测量数据,从而得到平面度误差的不确定度;通过设置实验对比,验证了蒙特卡罗法评定平面度不确定度的可靠性和准确性;该方法不需要求出数学模型中的传递系数,利用MATLAB软件很容易实现,为平面度误差测量结果不确定度评定提供了更加简便的方法,值得推广和应用。     

Unified uncertainty analysis by the mean value first order saddlepoint approximation

Uncertainties exist in products or systems widely. In general, uncertainties are classified as epistemic uncertainty or aleatory uncertainty. This paper proposes a unified uncertainty analysis (UUA) method based on the mean value first order saddlepoint approximation (MVFOSPA), denoted as MVFOSPA-UUA, to estimate the systems probabilities of failure considering both epistemic and aleatory uncertainties simultaneously. In this method, the input parameters with epistemic uncertainty are modeled using interval variables while input parameters with aleatory uncertainty are modeled using probability distribution or random variables. In order to calculate the lower and upper bounds of system probabilities of failure, both the best case and the worst case scenarios of the system performance function need to be considered, and the proposed MVFOSPA-UUA method can handle these two cases easily. The proposed method is demonstrated to be more efficient, robust and in some situations more accurate than the existing methods such as uncertainty analysis based on the first order reliability method. The proposed method is demonstrated using several examples.     

基于不确定性的数据融合系统性能评估

提出了一种基于不确定性的数据融合系统性能评估方法。数据融合的目的是为了提高融合后的信息量,信息不确定度的降低就相当于信息量的增加,本文从广义信息论出发度量信息不确定度,通过比较融合前后系统信息的不确定度来度量融合系统的性能。给出多目标系统中计算信息不确定度归一化变化的方法,实现了融合系统性能的定量评估。     

曲线拟合法失真度测量的不确定度分析

概述了失真度测量的应用和研究现状,介绍了基于曲线拟合法的失真度测量方法。结合失真度测量的实验,对曲线拟合法失真度测量中的不确定度影响因素进行了分析。针对数据采集卡和曲线拟合引入的不确定度难于分析求解的问题,基于失真度测量的误差修正公式和Parseval定理,重点研究了曲线拟合法失真度测量结果的不确定度分析方法。该方法为失真度曲线拟合测量结果不确定度评定提供了科学的依据。     

考虑认知不确定性的模型确认贝叶斯因子法*

针对认知不确定性条件下计算机建模仿真所面临的模型确认问题,提出一种结合了二阶概率法与区间数排序的改进贝叶斯模型确认方法。该方法首先采用二阶概率法对模型的不确定性进行量化,量化结果被做为先验模型输出,再基于实验数据对模型输出的先验概率密度进行贝叶斯更新,最后通过区间数排序的方式对比模型输出的后验和先验概率密度。由此所得的贝叶斯因子能够在模型存在认知不确定性的情况下为模型确认提供可信的结果。算例分析结果显示了本文方法的合理性。     

核电站重要仪表测量通道的不确定度计算方法

对核电厂重要仪表测量通道的不确定度计算的相关国内核安全法规要求、最新国际标准进行了研究,提出一套测量通道不确定度计算的方法。总结完善了计算方法中不确定度通道的类型、国产化仪表设备的不确定度项区分、环境条件区分以及整定值的确定等内容,演示了采用该方法进行仪表测量不确定度计算的过程,并给出了计算结果。通过相关计算,验证了该方法对维护核电厂原有安全基准的有效性。相比较于同类计算方法获得的结果,新计算方法具有明显的优越性。     

模糊理论在评价项目不确定性中的应用

项目活动的复杂性和动态性决定了项目的不确定性，概率及概率分布的观点比较适合于处理存在相关历史数据的项目的不确定性。面对很少有或根本没有可供参考的历史数据的项目，使用模糊方法处理不确定性是恰当的。文章使用模糊数来描述项目及活动的不确定性，并举出了一个具体算例。     

小型无人直升机反步自适应控制

针对具有强非线性、高度耦合以及参数不确定性特点的小型无人直升机系统,提出一种基于小脑模型关节控制器（Cerebellar Model Articulation Control,CMAC）神经网络的自适应反步控制方法,该方法采用小脑模型关节控制器神经网络在线学习系统不确定性以及反步控制中各阶虚拟控制量的导数信息,设计鲁棒控制项克服CMAC神经网络在线学习系统不确定性的误差,控制律由反步法回归递推得到。仿真结果表明,在模型参数不确定和存在较大误差的情况下,所设计的控制律具有理想的姿态跟踪性能以及良好的鲁棒性。     

飞灰含碳量的预测与测量不确定度的评定

由于传统人工灼烧失碳法对飞灰含碳量的测定存在严重滞后锅炉燃烧实际工况的问题,结合人工灼烧失碳法的测量原理和测定过程,将加灰量和灼烧失碳量作为数据点,采用最小二乘法拟合,建立飞灰含碳量的预测模型。此外,为了确保飞灰含碳量测定结果的准确可靠,逐项分析测量过程中的不确定度来源,根据测量不确定度的基本方法和程序,建立飞灰含碳量测量不确定度评定的数学模型,重点探讨最小二乘法拟合、质量称量、重复性测量引入的标准不确定度,合成不确定度和扩展不确定度。结果表明,飞灰含碳量的测定值和预测值两者的相关性强(相关系数为0.632)。当加灰量为1.5g时,灼烧失碳量的预示值为0.0687g,飞灰含碳量的预测值为4.58%,计算得出合成不确定度为0.0032g,扩展不确定度为0.0064g,验证了该方法的可行性和有效性。     

蒙特卡洛法在发动机推力测量不确定度评估中的应用

现行对发动机试验推力测量不确定度的评估一直采用GUM法,存在输入量和输出量概率分布假设以及非线性模型近似等问题,有一定的局限性;以发动机推力矢量测量为例,文中简述了压电式推力矢量测量的数学模型,运用GUM法对推力矢量参数的不确定度评估,同时分析了蒙特卡洛法的原理、具体评估过程和适用性,编制了软件,并将不确定度评估结果与GUM法评估结果进行对比;对比结果表明,在发动机试验推力矢量参数的不确定度评估过程中,蒙特卡洛法相比GUM法更为适用.     

Modelling of uncertain systems with application to robust process control

A method for black-box identification of uncertain systems is presented. The method identifies a nominal model and an uncertainty model set, consisting of unfalsified uncertainty models. Minimisation of a Chebyshev criterion leads to computationally favourable linear programming problems and allows the possibility to include a priori information in the form of linear constraints without making the computations more complex. Using data compression via correlation computations solves the computation problem associated with identifying unfalsified uncertainty models. The application of set-valued uncertainty models to robust process control is illustrated in a simulation study of robust model predictive control of a distillation column.     

Smith predictor design for robust performance

A method is outlined for designing Smith predictor controllers that provide robust performance despite real parameter uncertainties in the process model. Insight into the design process is gained by viewing the Smith predictor from the perspective of internal model control. Performance requirements are written in terms of a frequency-domain weight restricting the magnitude of the closed-loop sensitivity function. A general method for approximating multiple parameter uncertainties by a single multiplicative uncertainty is developed—an exact bound is derived for the magnitude of multiplicative uncertainty used to approximate simultaneous uncertainties in process gain, time-constant, and time-delay. Three different tuning methods are demonstrated; each is applied to a wide range of parameter uncertainties in a first-order with time-delay model. The first tuning method locates loop transfer-function uncertainty regions to test for robust performance—real parameter uncertainties are considered exactly. The second tuning method approximates real parameter uncertainties by multiplicative uncertainty and uses structured singular value analysis to guarantee robust performance. The third is a ‘quick design’ method that considers the unit magnitude crossing of the multiplicative uncertainty. Finally, the Smith predictor controller is compared with the structured-singular-value-optimal controller.     

Handling Uncertainty in the Development and Design of Chemical Processes

The paper presents a stochastic methodology for handling uncertainty in process development as part of a general framework for batch and continuous process models. The method combines systematic modelling procedures with Hammersley sampling based uncertainty analysis and a range of sample-based sensitivity analysis techniques which are used to quantify predicted performance uncertainty and identify key uncertainty contributions. The methodology was implemented on a batch chemical reactor process and some clear recommendations as to how to reduce the uncertainty in the main output variables are obtained.     

Prediction of industrial equipment Remaining Useful Life by fuzzy similarity and belief function theory

We develop a novel prognostic method for estimating the Remaining Useful Life (RUL) of industrial equipment and its uncertainty. The novelty of the work is the combined use of a fuzzy similarity method for the RUL prediction and of Belief Function Theory for uncertainty treatment. This latter allows estimating the uncertainty affecting the RUL predictions even in cases characterized by few available data, in which traditional uncertainty estimation methods tend to fail. From the practical point of view, the maintenance planner can define the maximum acceptable failure probability for the equipment of interest and is informed by the proposed prognostic method of the time at which this probability is exceeded, allowing the adoption of a predictive maintenance approach which takes into account RUL uncertainty. The method is applied to simulated data of creep growth in ferritic steel and to real data of filter clogging taken from a Boiling Water Reactor (BWR) condenser. The obtained results show the effectiveness of the proposed method for uncertainty treatment and its superiority to the Kernel Density Estimation (KDE) and the Mean-Variance Estimation (MVE) methods in terms of reliability and precision of the RUL prediction intervals.     

圆柱度误差评定中规范不确定度计算方法的研究

针对目前误差评定结果往往只提供测量不确定度的情况,根据新一代产品几何规范(GPS)不确定度体系,研究了圆柱度误差评定时规范不确定度的计算方法。基于最小区域法提出了圆柱度误差评定的数学模型,用改进粒子群算法得到圆柱度误差值,并通过研究影响规范不确定度每一个元素的传播系数及相关系数推导出了规范不确定度的详细计算公式,且以此为基础开发了圆柱度误差评定的图形界面。经实例验证,该方法可以在新一代GPS体系下准确、直观、方便地评定圆柱度误差。     

机电设备BIT 中传感层系统的不确定性动态分析􀀂

BIT(Built-in test)系统中传感层的不确定性反映了测试输出数据的可靠度，而数据的可信度影响着BIT系统进行决策的准确性和可信度，并且数据的不确定性是产生虚警的重要因素之一，对传感数据进行不确定性分析是获得数据可信度的有效的量化方法。传统的不确定性分析为静态分析过程，本文基于传感层系统的状态模式空间提出了传感层的不确定性动态分析方法，根据其可靠性模型，基于概率方法进行不确定性的动态计算，实现对传感层系统的执行和输出数据的可信度进行评估。同时对未覆盖故障集合对分析结果的影响进行了分析。最后对某电涡流位移传感器进行了不确定性分析的仿真。