Simultaneous data reconciliation and gross error detection for dynamic systems using particle filter and measurement test
Simultaneous data reconciliation and gross error detection for dynamic systems using particle filter and measurement test
复制标题
使用粒子滤波器和测量测试对动态系统进行同步数据协调和粗差检测
DOI：
10.1016/j.compchemeng.2014.06.014
复制
发表时间：
2014
影响因子：
4.3
通讯作者：
Chen Junghui
中科院分区：
文献类型：
--
作者：
Zhang Zhengjiang;Chen Junghui
良好的动态模型估计对于前馈和反馈控制、故障检测和系统优化都起着重要的作用。试图成功地实现模型估计往往受到严重的过程非线性，复杂的状态约束，系统建模误差，不可测量的扰动，和不规则的测量可能异常的行为。因此，同步数据协调和粗差检测（DRGED）的动态系统是基本的和重要的。本文提出了一种基于测量测试的粒子滤波算法（PFMT-DRGED）。该策略可以有效地解决非线性动态过程系统中含有粗差的测量数据的DRGED问题。PFMT-DRGED的性能证明通过两个统计性能指标的结果在一个经典的非线性动态系统。所提出的PFMT-DRGED应用于一个非线性动态系统和一个大规模的聚合过程的有效性进行了说明。
Good dynamic model estimation plays an important role for both feedforward and feedback control, fault detection, and system optimization. Attempts to successfully implement model estimators are often hindered by severe process nonlinearities, complicated state constraints, systematic modeling errors, unmeasurable perturbations, and irregular measurements with possibly abnormal behaviors. Thus, simultaneous data reconciliation and gross error detection (DRGED) for dynamic systems are fundamental and important. In this research, a novel particle filter (PF) algorithm based on the measurement test (MT) is used to solve the dynamic DRGED problem, called PFMT-DRGED. This strategy can effectively solve the DRGED problem through measurements that contain gross errors in the nonlinear dynamic process systems. The performance of PFMT-DRGED is demonstrated through the results of two statistical performance indices in a classical nonlinear dynamic system. The effectiveness of the proposed PFMT-DRGED applied to a nonlinear dynamic system and a large scale polymerization process is illustrated.