Error Compensation-based Time-Space Separation Modeling Method for Complex Distributed Parameter Processes

doi:10.1016/j.jprocont.2019.05.001

Return

Error Compensation-based Time-Space Separation Modeling Method for Complex Distributed Parameter Processes

2019-08-01

PRE

Xinjiang Lu *

Pingzhong He

Jie Xu

DOI：10.1016/j.jprocont.2019.05.001

Original

Original request for help

Save

Abstract

En 中文

Constructing a model for nonlinear distributed parameter systems (DPSs) is challenging due to their strong nonlinearity and spatiotemporal nature. As a result, most DPS modeling methods have low modeling accuracy for strongly nonlinear DPSs and this inaccuracy is primarily due to truncation errors and neglect of nonlinear dynamics. Here, an error compensation-based time-space separation modeling method is proposed to better resolve these limitations. We first constructed a Karkunen-Loeve and least squares support vector machine (KL-LS-SVM) time-space separation model to represent the main dynamic behavior. Simultaneously, we developed a spatiotemporal least squares support vector machine (LS-SVM) to compensate for any modeling errors due to either truncation or unknown nonlinear dynamics. These two models were then integrated to construct a complete spatiotemporal model, which allowed for the reconstruction of the DPSs. Performance analyses and experimental validation further showed that the proposed method can effectively model complex nonlinear DPSs and have better modeling ability than more commonly used DPSs modeling methods. (C) 2019 Elsevier Ltd. All rights reserved.

Keywords:

Distributed parameter system

spatiotemporal LS-SVM

modeling

partial differential equation

compensation

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.