基于鲁棒估计的动态数据校正方法

针对目前动态数据校正方法存在的缺陷，本文在以前的工作基础上把基于鲁棒估计原理构造的鲁棒估计函数用于含异常点类型的过失误差的数据校正，Monte Carlo模拟结果及对TE问题的校正计算结果表明，这种基于鲁棒估计的过换误差侦破和数据协调同步方法可以在得到协调数据的同时很准确的侦破和识别出测量数据中所含的过失误差，具有较强的优越性。     

基于鲁棒估计的动态传感数据校正方法

为实现IEEE1451网络化智能传感器输出数据与输入信号随时间的变化曲线保持一致,解决智能传感中的非线性问题,根据数据校正与数据回归的相似性,引入并改进在数据回归中应用较好鲁棒估计方法,结合非线性动态数据校正的DAEs模型,为传感器的校正引擎构建了相应的鲁棒估计目标函数。应用实例可得,校正后的结果与模型值的均方差MSE＆lt;0.003,表明在数据校正方法中引入鲁棒估计方法,可以使目标函数对偏离理想条件的数据不敏感,能显著减小异常数据点对传感数据校正的不利影响,实现高效的数据自校正。     

观测误差协方差估计下的集合鲁棒滤波数据同化方法

在数据同化方法中,观测误差协方差矩阵是相关的,且与时间和状态有一定的依赖性。针对这种相关特性,将鲁棒滤波方法与观测误差协方差估计方法相结合,得到随状态时间变化的观测误差协方差,提出一种带有观测误差估计的鲁棒数据同化新方法,更新观测误差协方差,改善估计效果。从分析误差协方差,转移矩阵特征值放大等角度优化同化方法。利用非线性Lorenz-96混沌系统,对三种不同优化角度下带有观测误差估计的鲁棒滤波和原鲁棒滤波方法的鲁棒性和同化精度进行评估,并比较分析了两种方法在模型误差、观测数目和性能水平系数变化时的性能。结果表明：观测误差估计技术能够提高状态估计的精确性,带有观测误差估计的鲁棒滤波对系统参数变化具有较好的鲁棒性。     

一种改进的基于鲁棒估计的动态数据校正方法

针对目前动态数据校正方法存在的缺陷,本文基于鲁棒估计的原理,提出一种新型的鲁棒估计函数,该函数物理概念清晰,参数调节灵活。基于此函数构造的动态数据校正方法(IRDR),在校正随机误差的同时,可以同步对异常点过失误差进行侦破和识别。CSTR仿真实例表明,该方法可以准确识别出系统所含的多个过失误差,校正结果偏差小,曲线平滑,具有较强优越性。     

基于鲁棒估计的氧化铝蒸发过程数据协调

针对氧化铝蒸发过程实时检测数据存在随机误差或显著误差、出口物料浓度检测滞后大,导致实测数据难以真实反映实际生产状态的现状,本文在蒸发过程多点稳态检测研究的基础上,利用基于核偏最小二乘法的出口物料浓度软测量结果提供的有效信息,建立以污染正态分布的鲁棒估计函数为优化目标的数据协调模型,并采用遗传算法求解获得蒸发过程的协调数据.实际计算结果表明,该协调模型能避免显著误差的影响,实现测量数据的可靠在线协调计算,为过程操作调节提供依据.     

一类非线性系统的误差轨迹跟踪鲁棒学习控制算法

针对一类含非参数不确定性的非线性系统,提出一种鲁棒迭代学习控制算法,该算法放宽了常规迭代学习控制方法的初始定位条件,迭代初值可任意取值.基于类Lyapunov方法设计误差轨迹跟踪控制器,通过鲁棒限幅学习机制对不确定性进行估计和补偿,能够在整个作业区间上实现误差对给定期望误差轨迹的精确跟踪,期望误差轨迹根据迭代起始时刻的误差值设置.利用期望误差轨迹的衰减性状,可使系统误差在预设的时间点后收敛于原点的邻域内,邻域半径的大小可根据需要任意设置.理论分析和仿真结果表明了控制方法的有效性.     

基于CKF的GPS定位误差估计及故障检测

针对卡尔曼滤波(KF,Kalman filter)算法无法解决非线性系统估计的问题和扩展卡尔曼滤波(EKF,extended Kalman filter)算法在GPS卫星导航定位误差估计中存在的线性化误差大、需求解繁琐的Jacobian矩阵等问题,将较新型的卡尔曼滤波算法——容积卡尔曼滤波(CKF,cubature Kalman filter)应用于GPS定位计算,在算法精度上与KF和EKF算法进行了比较,并利用定位误差进行GPS故障卫星的检测.利用实测导航电文数据进行实验,结果表明:CKF用于导航定位估计不仅精度高,而且实现简单,无可调参数,性能明显优于KF和EKF,并可进一步用于故障检测.     

观测迭代的插值滤波算法

针对非线性跟踪系统中由于弱观测性,大的初始化误差使的系统出现不稳定、跟踪收敛速度慢,鲁棒性能差的问题,本文在内插公式滤波器的基础上提出了基于观测迭代插值滤波器。该算法在插值滤波器基础上,利用观测迭代过程来取代单纯的近似条件估计进行预测,减小观测函数线性化所带来的误差影响具有更精确的状态和协方差估计性能。仿真结果表明该算法在大噪声和大初始化误差条件下拥有比传统算法更高的跟踪精度,和更快的收敛速度。     

变参考轨迹下的鲁棒迭代学习模型预测控制

迭代学习模型预测控制是针对间歇过程的先进控制方法.它能通过迭代高精度跟踪给定参考轨迹,并保证时域上的闭环稳定性.然而,现有的迭代学习模型预测控制算法大多基于线性/线性化系统,且没有考虑参考轨迹变化的情况.本文基于线性参变系统提出一种能有效跟踪变参考轨迹的鲁棒迭代学习模型预测控制算法.首先,采用线性参变模型准确涵盖原始非线性系统的动态特性.然后,将鲁棒H_∞控制与传统迭代学习模型预测控制相结合,抑制变参考轨迹带来的跟踪误差波动,通过优化线性矩阵不等式约束下的目标函数求得控制输入.深入分析了鲁棒迭代学习模型预测控制的鲁棒稳定性和迭代收敛性.最后,通过对数值例子和连续搅拌反应釜系统的仿真验证了所提出算法的有效性.     

非线性系统的鲁棒故障检测与诊断

研究了一类具有未建模动态或扰动的非线性系统的鲁棒故障检测与诊断问题,利用神 经网络、模糊系统或小波网络等对非线性故障模式进行在线逼近的方法进行故障诊断.第一步， 对用于鲁棒故障检测的观测器,建立了保证观测器稳定的增益阵的选择条件;第二步,若检测出 发生故障,则用神经网络、模糊系统或小波网络进行故障的在线估计,建立了估计误差界,结果 显示输出估计误差将收敛到由扰动上界或建模误差上界线性确定的范围内.     

Robust and reliable estimation via Unscented Recursive Nonlinear Dynamic Data Reconciliation

The quality of process data in a chemical plant significantly affects the performance and benefits gained from activities like performance monitoring, online optimization and control. Since many chemical processes often exhibit nonlinear dynamics, techniques like Extended Kalman Filter (EKF) and Nonlinear Dynamic Data Reconciliation (NDDR) have been developed to improve the data quality. There are various issues that arise with the use of either of these techniques: EKF cannot handle inequality or equality constraints, while the NDDR has high computational cost. Recently a recursive estimation technique for nonlinear dynamic processes has been proposed which combines the merits of EKF and NDDR techniques. This technique, named as Recursive Nonlinear Dynamic Data Reconciliation (RNDDR), provides state and parameter estimates that satisfy bounds and other constraints imposed on them. However, the estimate error covariance matrix in RNDDR is computed in the same manner as in EKF, that is, the effects of both nonlinearity and constraints are neglected in the computation of the estimate error covariance matrix.

A relatively new method known as the Unscented Kalman Filter has been developed for nonlinear processes, in which the statistical properties of the estimates are computed without resorting to linearization of the nonlinear equations. This leads to improved accuracy of the estimates. In this paper, we combine the merits of the Unscented Kalman Filter and the RNDDR to obtain the Unscented Recursive Nonlinear Dynamic Data Reconciliation (URNDDR) technique. This technique addresses all concerns arising due to the presence of nonlinearity and constraints within a recursive estimation framework, resulting in an efficient, accurate and stable method for real-time state and parameter estimation for nonlinear dynamic processes.     

Robust data reconciliation of combustion variables in multi-fuel fired industrial boilers

This paper introduces an application of simultaneous nonlinear data reconciliation and gross error detection for power plants utilizing a complex but computationally light first principle combustion model. Element and energy balances and robust techniques introduce nonlinearity and the consequent optimization problem is solved using nonlinear optimization. Data reconciliation improves estimation of process variables and enables improved sensor quality control and identification of process anomalies. The approach was applied to an industrial 200 MW_th fluidized bed boiler combusting wood, peat, bark, and slurry. The results indicate that the approach is valid and is able to perform in various process conditions. As the combustion model is generic, the method is applicable in any boiler environment.     

一种基于罚函数的数据协调模型

如果工业测量数据中存在显著误差,则传统的数据协调模型会将显著误差分散到各个测量值中去.针对于传统数据协调模型的缺陷,本文通过添加一个基于测量值比例关系上下限的约束条件,并利用罚函数的概念将物料平衡的约束条件以软约束的形式表示,建立一种新的数据协调模型.改进后的数据协调模型只会对含有显著误差的测量值给予较大的协调量,而使得显著误差对其他测量值协调结果的影响较小,具有较高的鲁棒性.基于工业实际生产模型的仿真试验证明基于该数据协调模型的协调结果,可直接利用测量残差检测法进行显著误差检测,具有较高的错误检出率,且第Ⅰ类错误率较低.     

一种用于数据校正的最小二乘与准最小二乘组合方法

过程系统的控制与优化要求可靠的过程数据。通过测量得到的过程数据含有随机误差和过失误差,采用数据校正技术可有效地减小过程测量数据的误差,从而提高过程控制与优化的准确性。针对传统基于最小二乘的数据校正方法:和基于准最小二乘的鲁棒数据校正方法:,分析了它们的优缺点,并提出了一种最小二乘与准最小二乘组合方法:。该方法:先采用准最小二乘估计器检测过失误差并剔除,然后再采用最小二乘估计器进行数据校正,可以综合前两种方法:各自的优点,使得数据校正结果:更加准确。将提出最小二乘与准最小二乘组合方法:应用于线性与非线性系统的数据校正中,通过校正结果:的比较说明此方法:的具有较好的过失误差检测能力和较准确的数据校正结果:。最后将此方法:应用于实际过程系统空气分离流程的数据校正中,结果:说明了此方法:的有效性。     

A novel robust data reconciliation method for industrial processes

Data reconciliation has played a significant role in rectifying process data which can meet the conservation laws in industrial processes. Generally, the actual measurements are often easily contaminated by different gross errors. Thus, it is essential to build robust data reconciliation methods to alleviate the impact of gross errors and provide accurate data. In this paper, a novel robust estimator is proposed to improve the robustness of data reconciliation method, which is based on a new robust estimation function. First, the main robust properties are analyzed with its objective and influence functions for the proposed robust estimator. Then, the effectiveness of the new robust data reconciliation method is demonstrated on a linear numerical case and a nonlinear example. Moreover, it is further used to a practical industrial evaporation production process, which also demonstrates that the process data can be better reconciled with the proposed robust estimator.     

A Robust Regression Model for Simultaneous Localization and Mapping in Autonomous Mobile Robot

Segment-based maps as sub-class of feature-based mapping have been widely applied in simultaneous localization and map building (SLAM) in autonomous mobile robots. In this paper, a robust regression model is proposed for segment extraction in static and dynamic environments. We adopt the MM-estimate to consider the noise of sensor data and the outliers that correspond to dynamic objects such as the people in motion. MM-estimates are interesting as they combine high efficiency and high breakdown point in a simple and intuitive way. Under the usual regularity conditions, including symmetric distribution of the errors, these estimates are strongly consistent and asymptotically normal. This robust regression technique is integrated with the extended Kalman filter (EKF) to build a consistent and globally accurate map. The EKF is used to estimate the pose of the robot and state of the segment feature. The underpinning experimental results that have been carried out in static and dynamic environments illustrate the performance of the proposed segment extraction method.     

基于粗大误差检测和补偿的改进型EKF动态目标跟踪算法

卡尔曼滤波作为当前动态目标跟踪中的常用滤波算法,研究其动态跟踪的准确性对于军事制导,交通导航等领域具有重大意义。针对动态系统目标跟踪观测过程中存在的坏值、静差和漂移三种粗大误差,基于传统扩展卡尔曼滤波（Extended Kalman Filter,EKF）算法框架,引入了一种粗大误差检测和补偿方法,实现了对动态系统观测值中粗大误差的准确辨识和优化补偿,使得扩展卡尔曼滤波能够结合粗大误差检测和补偿方法有效地排除观测值中的粗大误差,滤波后的状态估计值更加准确地逼近真实值。经过仿真实验和对比,提出的改进型EKF算法能有效地排除粗大误差观测值对状态预测过程的影响,并且实现了对动态系统目标的准确跟踪,这大大提高了动态目标跟踪的精确度。     

测量显著误差的F检验法

显著误差检测是数据校正技术中必不可少的一环,以往的显著误差检测方法绝大多数都是基于测量残差和约束残差这两个统计量展开研究的.基于测量残差的检测方法首先需要对测量数据进行数据协调,这就会将显著误差分散到各个测值中去,从而会对显著误差的位置做出错误的判断.基于约束残差的检测方法只能对节点的平衡性进行判断,而无法确定显著误差的具体发生位置.为此,本文通过构造一个基于测量值比例关系的F统计量,并与约束残差统计量相结合,对稳态过程中出现的显著误差进行检测.通过对工业数据的仿真结果表明此方法对显著误差十分敏感,其各项性能指标均符合实际工业要求,具有较高的可信度和可应用性.