基于状态跟踪的非线性工业系统全工况建模

doi:10.16182/j.issn1004731x.joss.201803010

摘要/Abstract

摘要： 提出一种基于状态跟踪的非线性工业系统全工况建模方法。针对历史数据量过大,建模数据筛选困难的问题,设计一滑动窗口筛选稳态数据,推导了窗口中标准差的快速递推算法;分析未知扰动对系统的影响机制,选取由动态回归稳态的数据建模,提出一种可消除扰动影响的数据驱动建模算法;利用过程工业大数据包含的模型信息,应用高次函数拟合各工况模型参数,提出一种基于特征参数的线性变参数传递函数模型。对某工业过程进行辨识,表明了有效性。

关键词: 过程工业大数据, 稳态工况筛选, 状态观测器, 非线性系统, 全工况建模

Abstract: From the prospective of industrial big data modeling, this paper presents a modeling method for nonlinear industrial system at all operating conditions based on state tracking. In view of large amount of historical data and the difficulty to screen the modeling data, a sliding window is designed to screen steady-state data. The fast calculation method for the standard deviation is deducted. The influence mechanism of unknown disturbance on the system is analyzed. The data segment, representing the system from dynamic state to stable state, is selected as the modeling data. A data-driven modeling algorithm, which can effectively eliminate the disturbance influence, is proposed. The model information contained in the process industry big data is adopted and the high order function is applied to fit the model parameters. A linear transfer function model with variable parameter based on the characteristic parameters is proposed. The effectiveness of the proposed method is verified by modeling an industrial process.

Key words: big data of process industries, steady state screening, state observer, nonlinear system, modeling at all operating conditions

中图分类号:

TP391.9

董泽, 尹二新. 基于状态跟踪的非线性工业系统全工况建模[J]. 系统仿真学报, 2018, 30(3): 846-856.

Dong Ze, Yin Erxin. Modeling of Nonlinear Industrial System at All Operating Conditions Based on State Tracking[J]. Journal of System Simulation, 2018, 30(3): 846-856.

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