Modeling of Nonlinear Industrial System at All Operating Conditions Based on State Tracking

doi:10.16182/j.issn1004731x.joss.201803010

Abstract

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

CLC Number:

TP391.9

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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